Analysis of the new technologies for industrial … · Analysis of the new technologies for...

POLITECNICO DI MILANO Scuola di ingegneria dell’informazione

Corso di Laurea Specialistica in Ingegneria dell’Automazione

Analysis of the new technologies for industrial maintenance

Relatore: Chiar.mo Prof. Marco Garetti

Correlatore: Ing. Luca Fumagalli

Tesi di laurea di: Marco Romanò Matricola: 707300

Anno accademico 2010/11

Innovation in maintenance

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Abstract (Italian) ..................................................................................................... 7 Abstract (English) ................................................................................................... 7 Estratto .................................................................................................................... 8 Summary ................................................................................................................11 Cap. 1 Introduction................................................................................................ 14

1.1 Definition .................................................................................................... 14 1.2 Maintenance policies................................................................................... 15

1.2.1 Corrective maintenance........................................................................ 15 1.2.2 Preventive maintenance ....................................................................... 16

Reference........................................................................................................... 24 Chap. 2 Surveys .................................................................................................... 26

2.1 Survey ......................................................................................................... 26 2.2 Italian situation............................................................................................ 26 2.3 North America............................................................................................. 30 2.4 Sweden ........................................................................................................ 31 2.5 Comparison ................................................................................................. 31 Reference........................................................................................................... 33

Cap. 3 Condition-based maintenance....................................................................35 3.1 Definition .................................................................................................... 35 3.2 Condition monitoring.................................................................................. 37 3.3 CBM steps................................................................................................... 37 3.5 Advantages and weak points ....................................................................... 38 3.5 Prognostic.................................................................................................... 39 3.6 Fault prediction methods............................................................................. 40 3.7 CBM tasks scheduling ................................................................................ 41 3.8 Common difficulties for the implementation of CBM................................ 43 Reference........................................................................................................... 46

Cap. 4 Standards.................................................................................................... 51 4.1 OSA-CBM .................................................................................................. 51 4.2 IEEE 1451 ................................................................................................... 53 4.3 ISO 13373-1 ................................................................................................ 53 4.4 IEEE 1232 ................................................................................................... 54 4.5 MIMOSA .................................................................................................... 54 4.6 ISO 17359 ................................................................................................... 55 4.7 ISO 13379 ................................................................................................... 55 4.8 ISO 13380 ................................................................................................... 56

Cap. 5 Predictive maintenance techniques............................................................ 57 5.1 Vibration monitoring................................................................................... 57

5.1.1 Vibration monitoring for fans............................................................... 58 5.1.2 Vibration monitoring for bearings........................................................ 58

5.2 Thermography ............................................................................................. 61 5.3 Oil analysis.................................................................................................. 61 5.4 Pressure/temperature/current monitoring.................................................... 62 5.5 Visual inspection ......................................................................................... 62 5.6 Noise ........................................................................................................... 63 5.7 Weight check ............................................................................................... 63 5.8 Current analysis........................................................................................... 63

5.8.1 NILM ................................................................................................... 64


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5.8.2 Electrical signature analysis................................................................. 66 5.8.3 Rotor analysis....................................................................................... 70

5.9 Process parameters check............................................................................ 71 Reference........................................................................................................... 72

Cap. 6 Smart sensors............................................................................................. 75 6.1 Description .................................................................................................. 75 6.2 Functionality ............................................................................................... 76

6.2.1 Signal processing ................................................................................. 76 6.2.2 Digital control and manipulation ......................................................... 77 6.2.3 Communication and bus interaction .................................................... 78

6.4 Characteristics ............................................................................................. 78 6.5 Sensor communication interface................................................................. 81

6.5.1 Wireless technologies...........................................................................82 6.5.1.1 Wireless network Topology............................................................... 84 6.5.1.2 Wireless WAN technologies.............................................................. 87

6.5.2 Wired technologies................................................................................... 88 6.5.3 Communication protocols ........................................................................ 89 6.5.4 Initiatives.................................................................................................. 91

6.5.4.1 Industrial initiatives........................................................................... 91 6.5.4.2 Academic initiatives..........................................................................91

6.6 RFID............................................................................................................ 92 6.7 Standards ..................................................................................................... 92

6.7.1 IEEE 1451 ............................................................................................ 92 6.8 Smart bearings............................................................................................. 97 6.9 Consideration about the smart sensors........................................................ 98 Reference......................................................................................................... 100

Cap. 7 Handheld devices..................................................................................... 103 7.1 Description ................................................................................................ 103 7.2 Handheld devices use cases ...................................................................... 104 7.3 Augmented reality ..................................................................................... 106 Reference......................................................................................................... 108

Cap. 8 Analysis of an industrial case ...................................................................110 8.1 Manufacturer company profile...................................................................110 8.2 Machine 1...................................................................................................114

8.2.1 Machine description............................................................................114 8.2.2 Prescribed maintenance.......................................................................116 8.2.3 Analysis: possible failure of the machine........................................... 121 8.2.4 Actual maintenance activities on Machine 1...................................... 123 9.2.5 Possible maintenance innovations...................................................... 124 8.2.6 Why these solution are not applied .................................................... 126 8.2.7 Possible improvements ...................................................................... 128

8.3 Machine 2.................................................................................................. 130 8.3.1 Machine description........................................................................... 130 8.3.2 Prescribed maintenance...................................................................... 134 8.3.3 Possible failures ................................................................................. 137 8.3.4 Actual maintenance status.................................................................. 139 8.3.5 Possible maintenance innovations...................................................... 140 8.3.6 Why these solution are not applied .................................................... 142


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8.3.7 More suitable innovation.................................................................... 144 Cap. 9 Conclusion ............................................................................................... 146 References ........................................................................................................... 149


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Table of figures Figure 1: The bathtub curve .................................................................................. 17 Figure 2: Reliability and MTBF(W.R.Wessels, 2003) .......................................... 18 Figure 3: Availability comparison (W.R.Wessels, 2003) ...................................... 19 Figure 4: P-F curve ............................................................................................... 36 Figure 5: diagnostics and prognostics are based on multi-source data (VTT, 2006)............................................................................................................................... 39 Figure 6: Layers for the identification of a fault ................................................... 40 Figure 7: Example of vibration analisys for bearings ........................................... 59 Figure 8: Examples of mechanical problems of the bearings ............................... 60 Figure 9: Star network topology ........................................................................... 85 Figure 10: Mesh network topology....................................................................... 86 Figure 11: Hybrid network topology..................................................................... 87 Figure 12: Wireless sensor data schematic............................................................ 93 Figure 13: Smart sensor software architecture...................................................... 96 Figure 14: Structure of the smart bearing .............................................................98 Figure 15: Example of a dispensing system.........................................................111 Figure 16: Rotor impregnation.............................................................................111 Figure 17: Plasma system ....................................................................................112 Figure 18: Heatstake system ................................................................................113 Figure 19: View of the machine 1 ........................................................................115 Figure 20: Footprint of the machine 1..................................................................116 Figure 21: Top view of machine 2 ......................................................................131 Figure 22: Lateral view of machine 2 ................................................................. 132 Figure 23: Side view of machine 2 .....................................................................133 Figure 24: Camera picture for piece measurement ............................................. 133


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List of table Table 1: Wireless technologies characteristics ...................................................... 84 Table 2: Fieldbuses characteristics........................................................................ 91 Table 3: TEDS information................................................................................... 95 Table 4: Prescribed maintenance for machine 1 ................................................. 120 Table 5: Prescribed maintenance for machine 2 ................................................. 134 Table 6: Scheduled part maintenance/exchange ................................................. 136


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Abstract (Italian) Lo scopo di questo lavoro è di analizzare le nuove tecnologie e servizi relativi alla manutenzione e alle attività connesse ad essa.

Al giorno d'oggi le aziende devono competere nel mercato globale, è quindi necessario ridurre i costi di produzione per poter mantere i prezzi bassi e mantenere la propria quota di mercato.

La manutenzione è un processo aziendale che è in genere considerato solo un costo per l'azienda. Tuttavia è generalmente uno dei processi con la più bassa efficienza. La manutenzione è inoltre generalmente gestita con metodi ormai superati rispetto allo stato dell’arte scientifico e sono presenti numerosi sprechi, c'è quindi ampio margine di miglioramento.

In questa tesi vengono analizzati i più recenti argomenti di ricerca legati alla manutenzione e relativi a nuove soluzioni tecnologie, facendo un'analisi della letteratura, vengono poi presentati due casi di studio reali, viene descritto lo stato attuale della manutenzione di due macchine e viene ipotizzata l'implementazione delle tecnologie descritte, viene infine fatta un'analisi critica delle ragioni per cui non sono implementate attualmente.

Abstract (English) The aim of this thesis is to analyze the new technologies and methods for the industrial maintenance and to the activities related to it.

Nowadays the companies have to compete in a worldwide market, it is necessary to reduce the production costs to be able to keep a competitive price and hold the market share.

The maintenance is one of the process inside a company that is generally considered only as a cost, because many think it does not generate any return of the investments. Nevertheless maintenance is one process with the lowest efficiency.

The maintenance is generally managed with old methods compared with the present scientific state of the art and there are several wastes, thus there is a great margin for improvements.

In this thesis the latest research topics related to the industrial maintenance are analyzed in a literature analysis, two real case studies are presented, the actual maintenance management of the machine is described, the implementation of the new technologies is hypothesized and in the end a critic analysis is carried out to show the reason why these innovation are not used.


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Estratto

Al giorno d'oggi le società dovendo competere in un mercato globale, devono contenere i costi di produzione per poter ridurre i prezzi oppure mantenere o incrementare i propri margini di profitto.

La manutenzione è una delle attività che rappresenta un costo per l'azienda ed è anche una delle attività in cui ci sono ampi margini di miglioramento.

L'obiettivo di questo elaborato è di analizzare le nuove tecnologie e servizi che possono migliorare la gestione della manutenzione e le sue varie attività.

Per fare ciò si è deciso di analizzare gli articoli pubblicati nelle varie riviste scientifiche di settore, un uso reale di queste tecnologie è stato considerato oltre alle opinioni dei ricercatori.

Per ridurre i costi legati alla manutenzione e allo stesso tempo garantire una buona qualità e disponibilità dei macchinari, è necessario tagliare gli sprechi che al momento sono presenti; per ottenere questo risultato è necessario agire su tutti gli aspetti legati alla gestione della manutenzione.

Il primo passo è garantire che tutte le persone che sono coinvolte abbiano accesso a tutte le informazioni di cui necessitano, ciò non è limitato al personale della manutenzione ma è esteso a tutte le persone o dipartimenti che sono legati a questo processo, ad esempio il responsabile degli acquisti, il manager della produzione ma anche l'operatore che lavora sulla macchina possono trarre vantaggio dalla maggiore disponibilità di informazioni.

E' necessario che un sistema informativo sia distribuito ovunque nella fabbrica per poter acquisire tutti i dati che sono prodotti nei vari processi tra cui l'amministrazione, la produzione e la manutenzione. Questi dati vanno poi analizzati e processati e le informazioni estratte possono essere poi fornite alle rispettive persone interessate.

Ogni processo e dipartimento utilizza a differenti tecnologie, queste devono essere capaci di comunicare le une con le altre o con un sistema che sia in grado di gestire la comunicazione tra i vari sistemi, questo però comporta l'aumento della complessità del sistema informativo.

La capacità di avere le informazioni necessarie al momento giusto permette alle persone incaricate di prendere le decisioni di fare le scelte giuste: questo ciò permette di tagliare i costi legati agli errori e all'acquisto di parti di ricambio errate.

Una grossa parte dei costi lagati alla manutenzione possono essere ricondotti alla assenza di produzione durante il periodo in cui la macchina è ferma in seguito a un guasto o alla sostituzione non necessaria di componenti.

Uno degli argomenti di ricerca più interessanti è la CBM (Condition Based


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Maintenance), il cui scopo è di identificare indizi di un possibile guasto,(.) grazie a queste informazioni i componenti possono essere sostituiti prima del loro guasto, evitando la mancata produzione, inoltre i componenti possono essere usati per quasi tutta la loro vita utile evitando di sostiuirli troppo presto.

La complessità e il grado di automazione dei sistemi CBM è aumentata sempre di più negli ultimi anni e la tendenza è di proseguire in questa direzione.

Al momento nessun metodo è in grado di soddisfare tutti i requisiti di un buon sistema diagnostico, per questo motivo sistemi ibridi con differenti algoritmi per la soluzione del problema possono essere una buona soluzione per gestire gli scenari complessi di un problema diagnostico in un impianto industriale.

Le prossime tecnologie renderanno possibile l'utilizzo del CBM per impianti che al momento hanno una complessità troppo elevata (per differenti motivi) per l'applicazione dei sistemi CBM attuali.

Grazie a questo la gestione della manutenzione sarà resa più semplice e permetterà anche di organizzare le varie attività di manutenzione in modo da minimizzare i tempi di fermo macchina.

Al momento la diffusione del CBM è abbastanza limitata visto che è applicata solo ad alcune macchine, ma in futuro la sua diffusione aumenterà grazie alla possibilità di gestire scenari più complessi, di avere procedure più automatizzate e di rendere la gestione di tutte le attività legate alla manutenzione più semplice.

Per supportare il CBM nell'identificazione dei guasti sono necessarie anche le informazioni sullo stato attuale dello stabilimento, la diagnosi deve essere integrata con gli altri processi aziendali.

Metodi qualitativi e quantitativi possono essere combinati per una più accurata identificazione degli indicatori di un guasto.

Per fare questo è necessaria una grossa mole di dati che devono essere il più accurati possibile, specialmente quelli legati agli eventi.

Un altro importante argomento di ricerca è quello legato ai sensore; l'importanza dei sensori è facilmente intuibile poichétutte le informazioni legate allo stato del sistema derivano in modo diretto o indiretto dai valori acquisiti dai sensori.

Sono state sviluppate nuove tecnologie per l'acquisizione affidabile di dati in linea e nuovi algoritmi per il analizzare in modo efficiente e rapido i segnali.

L'analisi e il raggruppamento dei segnali saranno sempre di più svolti dai sensori, in questo modo è possibile ridurre i costi, il consumo energetico e le risorse necessarie e allo stesso tempo incrementare le prestazioni e la precisione.

L'uso di reti wireless per il trasferimento delle informazioni tra i sensori e il sistema di acquisizione permette una maggiore flessibilità nella disposizione dei sensori e riduce is costi dovuti al cablaggio.

Grazie al rapido sviluppo dei MEMS (micro-electro-mechanical system) è


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possibile creare device intelligenti che possono acquisire il segnali direttamente in digitale e che hanno anche la capacità di monitorare continuamente il loro stato.

Per gestire questa accresciuta disponibilità di informazioni il sistema computerizzato di gestione della manutenzione (CMMS computerized maintenance management system) deve essere migliorato. Inoltre sono necessari nuovi e più potenti metodi per estrarre, processare e interpretare le informazioni contenute nei dati acquisiti dai sensori.

Lo scopo di tutto questo è non solo l'identificazione del guasto, ma anche della causa per poter definire un miglior piano di manutenzione ed evitare quando possibile futuri guasti.

L'ultimo interessante ambito di ricerca riguarda l'uso di dispositivi elettronici come ausilio alle operazioni di manutenzione.

Grazie a questi dispositivi l'operatore può accedere alle informazioni di cui necessita come ad esempio lo schema elettrico, i manuali o particolari tecnici; può inoltre collegarsi direttamente ai sensori e fare delle misurazioni, confrontarle con quelle relative alle ispezioni precedenti e identificare segnali di degrado.

Tutto questo in modo quasi istantaneo senza dover perdere tempo per cercare manuali o fogli intorno alla macchina.

L'obiettivo di questo lavoro è di analizzare gli studi legati alla manutenzione e le tecnologie sopra menzionate per descrivere l'evoluzione della gestione della manutenzione dal punto di vista dell'ICT.


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Summary

Nowadays, companies have to compete at worldwide level and they have to reduce their production cost, in order to keep the product at low prices, keeping or increasing their profit margin.

Maintenance is one of the activities that generate cost for a company. Nevertheless maintenance practices in the company often reserve margin for improvement.

Indeed the objective of this research is to analyse the new technologies and services that can improve maintenance management and maintenance tasks and then link them to a practical industrial case.

The mainly methodology adopted to make this research is literature analysis. Nevertheless, practical implication of use of technology is considered beside the different researchers’ opinion and comments of practical use of the analyzed technologies is considered when addressing the industrial case

To reduce the cost of the maintenance and at the same time assure a good service and availability, the first point is to provide to all the people involved in the process the data that they need, this is not limited only at the maintenance personnel but also the people in the purchasing department, the production manager and even the operator of the production line that can receive benefits from the availability of this information.

For example, it is advised that an ICT system is spread everywhere in the shop floor, in order to collect the data that are produced within different process such as the business, operation and maintenance, analyze and process them and give the information of interest to the correct people.

Each involved process utilizes a number of different technologies, all of these must be able to communicate with each other and this increases the complexity of the ICT environment.

The correct information at the right time will help people to take better decision, this will cut the cost connected to the wrong actions carried out and, for instance, the purchasing of useless spare parts.

The big part of the cost of the maintenance is either due to the lack of production for the down time after a fault or the unnecessary replacement of the components.

One of the most interesting research topic seems to be the CBM (Condition Based Maintenance), the goal of this method is to identify the clues of an incoming failure and give the information to the right person so the part can be changed before the failure, avoiding the down-time and, at the same time, without changing the part too early, so almost all the remaining useful life of the component can be used.


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The complexity and the grade of automation in CBM systems are increasing and the trend will continue in this direction.

Up to now no method alone is able to meet al. the requirements of a good diagnostic system so hybrid system with parallel ways of reasoning can be an attractive idea to handle a complex and industrial scale diagnostic problem.

According to the literature, in the future the emerging technologies will make possible to use CBM for plant that, at the moment, seem too complex (for many different reasons) for applying CBM. This will make maintenance management of the plant easier, providing all the benefits of many maintenance tasks carried out at the right time.

With the possibility to handle more complex scenarios, to have more automated procedures and the simplification of the management, the use in the industry of CBM will probably increase, while at the moment, CBM is applied in the most of the company solely on few machine or not applied at all.

To support the CBM in the identification of the faults the diagnosis of the plant status must be improved.

The diagnosis should be integrated with other process operations, the advantages of the qualitative and quantitative methods can be combined to be able to identify the early indicators of a fault. To this end, a lot of information is required, this information, especially the event related one, must be accurate.

Another interesting topic in the research is related to the sensors. All the information are provided to the system directly or indirectly by the sensors.

New sensors techniques are envisioned to be developed for robust on-line data acquisition and new algorithms for efficient and fast signal process.

Data processing and sensor fusion will be moved at the sensor node level, in order to reduce costs, power consumption and resources and at the same time increase the performances and the accuracy.

Furthermore, the use of wireless networks should allow more flexibility in the placement of the sensors and avoid the cost of the wired networks.

The CMMS (computerized maintenance management system) must be enhanced to be able to handle all these data and new and more powerful methods are required to extract, process and interpret the information contained in the data acquired by the sensors.

The goal is to identify not only the fault, but also the cause and help to define more efficient maintenance plans.

The last interesting topic is the use of PDAs (Personal Digital Assistant) or handheld devices as a support for the maintenance personnel during their duties.

The maintenance operator can access the needed information regarding the machines like electrical schematics and technical drawings, check the status of the


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sensors and read all the data regarding them, simply from his handheld device, without losing time to search for the right information.

The objective of this work is to analyze maintenance related studies and technologies above mentioned and to outline the evolution of the maintenance management from the ICT point of view. Two industrial cases are presented to show the problems in the practical use of such innovations.


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Cap. 1 Introduction

A foreword about what is maintenance is provided in this chapter to present the general scope of the investigation and provide the reader with the basics of maintenance management in order to introduce the main parts of the dissertation.

1.1 Definition

In OECD’s (Organization for Economic Co-operation and Development) resolution of 1963, maintenance was defined as “a business function entrusted with the constant control of the facilities and all the repair work and services necessary to ensure the smooth running and good state of conservation of the production facilities, services and equipment of the plant”.

The European Standards Committee (CEN) defined maintenance in its standard project WI 319-007 (1997) as “the grouping of all the technical, administrative and management actions taken during the lifecycle of a product in order to maintain or restore it in a state in which it can perform the required task, for which it was designed” (see also EN 13306:2001).

Maintenance commission of UNI (Italian Organization for Standardization) defined maintenance as "a combination of all technical and administrative actions, including supervision actions, intended to maintain or restore an entity in a state where they can perform the required function" (UNI 9910 and UNI 10147) 15 years ago.

In 2003 this norm was replaced by the norm UNI EN 13306, now defining maintenance as "a combination of all the technical, administrative and management activities planned during the life cycle of an entity, to keep it or return it in a state where they can perform the required function".

According to R. Keith Mobley (2002) the major part of the total operating costs for all the manufacturing or production plants in the US is maintenance.

The maintenance costs can bear on the product cost for a percentage that goes from 15% (food or related products) up to 60% (iron, steel, paper and other heavy industries) and can occupy a significant amount of the work force (e.g. up to 30% in the chemical process industry G. Waeyenbergh et al., 2002).

It is worth considering that these percentages include also the expense for the modification or improvement of the machines, this due to the fact that these activities are carried out normally by the maintenance personnel and normally


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these activities are not allocated in the correct cost center but are considered as maintenance on the asset.

From the same reference R. Keith Mobley (2002), according to a survey concerning maintenance management effectiveness, one third of the maintenance expenses (33%!) is wasted in improperly or unnecessary actions.

In the actual situation, where a company has to compete in a global market with competitors that have the production in some country where the manwork cost is cheaper or the environmental laws are less strict it is easy to see that cutting the maintenance wastes can reduce the cost of the products without affecting the quality and allow the company to regain the lost competitiveness.

Thanks to the developments in the electronics in the last decades, the machines are now equipped with microprocessor based controller instead of the old electromechanical systems, the computational power and memory available for the programming are continuously increasing, and also the capability to communicate and exchange information between the systems is continuously developing.

Thus, it is now possible to have a better knowledge of the status of the machine or its components, this has brought some advantages for the maintenance.

1.2 Maintenance policies In this paragraph the different maintenance policies are presented, their advantages and disadvantages are highlighted.

1.2.1 Corrective maintenance UNI norm defines corrective maintenance as “a maintenance performed following a failure intended to bring an entity in the state where it can perform the required function”

The corrective maintenance is the easiest policy that is possible to apply on a machine, when the machine is working then no actions are performed, when it is broken it will be repaired. It is the first maintenance strategy appeared in industry

A plant that is using this run-to-failure management does not spend any money on maintenance until it is necessary. In reality no plant is managed with only corrective maintenance, a basic group of preventive tasks are carried out on the machine like lubrication and small adjustments.


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Benefits:

• low direct costs,

• the low need of organizational structure

• no planning necessary

Disadvantages:

• lack of failure notice,

• the need of a oversized spare part warehouse

• high machine downtime

• low availability

• bad use of the maintenance personnel

• low control on costs

• high overtime labor cost

To be able to react rapidly to a failure an extensive spare parts inventories must be maintained, it must include all the major components for all the critical equipment and the maintenance personnel must be available and able to locate and identify the cause of the fault rapidly.

Due to the high downtime of the equipment the cost of this method is usually high, according to an analysis the cost of the a repair performed as a reaction to a fault is in average three times more expensive than the same repair made as a preventive action or scheduled. Being able to schedule a repair minimizes the repair time and the labor cost. Anyway it can be cost-effective in certain cases (Alsyouf, 2007; Kelly, 1997; Pintelon and Gelders, 1992) and when the profit margins are large (Sharma et al., 2005).

Global competition and the reduction of profit margin are forcing maintenance manager to apply more effective maintenance strategies nowadays.

1.2.2 Preventive maintenance The UNI definition of preventive maintenance is: “maintenance performed at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or degradation of operating conditions of an entity“.

All the preventive maintenance methods rely over the assumption that statistically the lifetime of a component or the failures on a machine have a standard behavior that can be identified, normally it can be described as a bathtub curve (see figure 1). It can be explained in the following way, a new component or a new machine


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has a high probability of failure due to installation problems during the first period of operation, after this initial period the probability of a failure is low for the normal life period. After this period the probability of failures increases rapidly.

Figure 1: The bathtub curve

In preventive maintenance the machine is repaired or the component is replaced based on the MTBF statistic or when the wear out signs start to show.

The scheduled maintenance interval (τ) is the time between two subsequent repair or replacement maintenance actions. The number of intervals expected over the life of the system (k) is an integer value. The time since the last scheduled maintenance interval is the independent variable (t) minus the cumulative time preceding the last scheduled maintenance interval, kτ. The comparison between the reliability function and MTBF for a system without a scheduled maintenance interval and the reliability function and MTBF for a system with a scheduled maintenance interval is shown in Figure 2 Reliability and MTBF.


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Figure 2: Reliability and MTBF(W.R.Wessels, 2003)

It is graphically evident that the reliability for a system that implements a scheduled maintenance in which the components are replaced and repaired prior to failure is significantly better than the reliability of a system that is allowed to run to failure. Since the MTBF (θ) is the indefinite integral of the reliability function it is also evident that the MTBF for a system that implements a scheduled maintenance is significantly improved over the MTBF for a system which is allowed to run to failure.

The comparison between the availability function for a system with and without a scheduled maintenance program is shown in the figure 3.


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Figure 3: Availability comparison (W.R.Wessels, 2003)

Theoretically the availability of a system with a scheduled maintenance program declines in a small magnitude from one scheduled maintenance activity to the next and as the system is repeatedly restored the availability returns to unity. The magnitude of the decreases in availability over time between scheduled maintenance intervals is justified by the assumption that the decrease for any interval is comparable to the decrease from the condition of the system when new. The availability over time for a system that does not implement a scheduled maintenance interval shows a gradual decline in overall availability of the system. The increases in availability following each maintenance action does not reach unity because the system is not restored by the maintenance action but only the component that has failed is removed and replaced so the other parts that are near to a failure state remain in their place.

The advantages of the preventive maintenance are:

• Management control: preventive maintenance can be planned unlike reactive maintenance, workloads can be scheduled so that equipment is available for preventive activities at reasonable times.

• Overtime: overtime can be reduced or eliminated. Surprises are reduced. Work can be performed when convenient; however, a proper distribution of maintenance tasks is required to ensure that all work is completed without excessive use of overtime.


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• Parts inventories: preventive maintenance approach allows the planning of which parts are going to be required and when, those material requirements may be anticipated to be sure that they are on hand for the event. A smaller stock of parts is required.

• Standby equipment: with high demand for production and low equipment availability, reserve, standby equipment is often required in case of breakdowns. Some backup may still be required with preventive maintenance, but the need and investment will certainly be reduced.

• Safety and pollution: if no preventive inspections or built-in detection devices are used, equipment can deteriorate to a point where it is unsafe or may spew forth pollutants. A good detection system catches degrading performance before it reaches too low a level.

• Quality: good preventive maintenance helps ensure costant quality output. Tolerances are maintained within control limits. Naturally, productivity is improved and the investment in preventive maintenance pays off with increased revenues.

And the main disadvantages are:

• Potential damage: every time a person touches a piece of equipment, damage can occur through neglect, ignorance, abuse, or incorrect procedures.

• Infant mortality: new parts and consumables have a higher probability of being defective or failing than exists with the materials that are already in use. Replacement parts are too often not subjected to the same quality assurance and reliability tests as parts that are put into new equipment.

• Parts use: replacing parts at preplanned preventive maintenance intervals, rather than waiting until a failure occurs, will obviously terminate that part’s useful life before failure and therefore require more parts. This is part of the trade-off among parts, labor, and downtime, of which the cost of parts will usually be the smallest component.

• Access to equipment: One of the major challenges when production is at a high rate is for maintenance to gain access to equipment in order to perform preventive maintenance tasks. This access will be required more often than it is with breakdown-driven maintenance. A good program requires the support of production, with immediate notification of any potential problems and willingness to coordinate equipment availability for inspections and necessary tasks.

The preventive maintenance can be classified in be of 3 types: time based,


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condition based or predictive.

1.2.2.1 Time based maintenance

Time Based Maintenance (TBM) is the easiest of the preventive maintenance policies, it consist of periodic maintenance tasks carried out according to a defined timeline. When the interval is elapsed since the last maintenance action on the component the component is repaired or exchanged without regard on its actual wear level, this to maintain always the component in the normal operation period on the bathtub curve.

There are two different approaches for this method, in the first the part is exchanged at constant intervals without caring if the component has worked or not, in the second one the exchange is based on the real age of the component counting the effective time that it has worked (Waeyenbergh and Pintelon, 2002; Kumar, 1996)

Benefits:

• easy planning and control

• possibility to schedule maintenance personnel tasks

• possibility to schedule the downtime of machine

• optimization of the stock of spare parts

Disadvantages:

• necessity of an accurate model or experience to achieve best results

• parts can be exchanged also if it is not necessary

This method it is relatively simple to implement, if the wear out of a part is constant or if a time interval between the failures can be identified this method can guarantee good results with low costs.

Unfortunately normally this is not possible and it is difficult to identify a model accurate enough to be sure to carry out the maintenance tasks on the correct time so the result is that the interval is shorter than the optimum and this lead to an high number of interventions (thus higher direct costs) or longer than the optimal so an higher number of failures (thus higher hidden costs).

Normally the manufacturer of the machine prepares a list with the maintenance actions that have to be carried out and their interval and this is included in the documentation of the equipment. This list can be a good point to start for the scheduling, these intervals are usually shorter than necessary because the producer does not know the exact condition of work of the machine and also he want to avoid complains from the customer. With the experience and the knowledge


22

acquired with the time working on the machine the intervals can be adjusted.

1.2.2.2 Condition based maintenance

UNI 10147 norm defines Condition Based Maintenance (CBM) as “preventive maintenance subordinated to achieve predetermined threshold value”.

The corrective maintenance is basically a set of actions carried out according to the actual condition of the machine or of the component, the idea underneath this method is that indicative prognostic parameters exist, can be detected and used to quantify the possibility of a failure before its occurrence.

The actual status of the equipment is obtained from sensors or measurements taken by the operator, these information are processed to check if the component performances deviates from the acceptable performance level and thus this can be a symptom of an incoming failure.

The common problems of equipments are ageing and deterioration, these trends can be identified through trend analysis of the equipment condition data and this information can be used to recognize when the component is near to the end of its life.

The status of the machine can be evaluated continuously or on time interval

Advantages:




• exchange of the components only when needed

• early identification of the faults

Disadvantages:

• difficult to implement

• requires a deep knowledge of the equipment to identify the parts that need to be observed

This method has a very big potential, the faults are identified early before the failure, thus is possible to avoid the stop of the machine, reduce the spare part warehouse and plan the maintenance actions.

Unfortunately the implementation is not easy, a deep knowledge is required to find which components need to be checked, where the sensor must be placed and especially for complex machines the failure cannot be reconducted straightforwardly to one abnormal trend but to a combination of small changes in


23

the acquired data.

1.2.2.3 Predictive maintenance

UNI norm defines Predictive Maintenance as “preventive maintenance carried out following the detection and measurement of one or more parameters and extrapolation of remaining time before failure with appropriate models”.

This method is similar to the Condition Based Maintenance but extends its capabilities to predict the future status of the equipment.

The data acquired from the machine are analyzed in order to find a possible temporal trend and so be able to predict when the monitored value will reach or exceed the defined threshold.

Advantages:




• exchange of the components only when needed

• early identification of the faults

Disadvantages:

• difficult to implement

• requires a deep knowledge of the equipment to identify the parts that need to be observed

This method gives good results in systems where faults are preceded by progressive degradation, the identification and quantification of this trend and the successive analysis will give the possibility to know with a good approximation the remaining life of the component.

As for the Condition Based Maintenance (of which this method can be considered an extension) the implementation is not easy, the amount of data required for the identification of the trend is big and a good knowledge of the machine is required.


24

Reference CONTENTS REFERENCES PUBLIC

ATION

YEAR

WORK

CONTRIBUTION

An introduction to predictive

maintenance Second Edition

R. Keith Mobley

Butterworth-Heinemann

2002 Specific knowledge

A framework for maintenance concept

development

Geert Waeyenbergh, Liliane Pintelon

Int. J. Production Economics 77 pp. 299-313


The role of maintenance in

improving companies’ productivity and

profitability.

Alsyouf I.

International Journal of Production Economics, 105,

pp. 70–78


Gestión del mantenimiento

industrial

Kelly A. and Harris M.J

Publicaciones Fundación Repsol, Madrid.


Maintenance Management Decision

Making

Pintelon, L., Gelders, L.F.

European Journal of Operational Research 58,

301–317


Reliability analysis and maintenance

scheduling considering operation

conditions.

Kumar, D.

Doctoral Thesis, Lulea University of Technology,

Sweeden.


Maintenance management:

literature review and directions

Amik Garg and S.G. Deshmukh

Journal of Quality in Maintenance Engineering

Vol. 12 No. 3, 2006pp. 205-238



25

Contracting out maintenance and plan

for future research

H.H. Martin

Journal of Quality in Maintenance Engineering,

Vol. 3 No. 2, 1997,pp. 81-90.



26

Chap. 2 Surveys This chapter presents the status of the implementation of the maintenance policies and programs described in the previous chapter in Italy and then the Italian situation is compared with other countries.

2.1 Survey A survey is a method used to collect in a systematic way, information from a sample of individuals. Although most people are familiar with public opinion surveys that are reported in the press, most surveys are not public opinion polls (such as political polling), but are used for scientific purposes. Surveys provide important information for all kinds of research fields.

Since survey research is always based on a sample of the population, the success of the research is dependent on the representativeness of the population of concern.

2.2 Italian situation A distinctive feature of the Italian manufacturing companies is the size, almost the 94% of the manufacturing firms have less than 20 employees, 4% have from 20 to 50 employees and only the 2% have more than 50 employees but they provide work for the 25% of the labor force in the manufacturing sector.

A survey on maintenance management in small and medium firms (Cattaneo, 2000) was carried out by AIMAN, the Italian Maintenance Society, in the year 2000. 174 companies with up to 200 employees were involved in this survey; the firms belong to mainly to the mechanical and metal working sectors and to the chemical and pharmaceutical sectors. The survey highlighted that an actual maintenance function exists in about 20 per cent of the micro-firms (with less than 15 employees), in about 50 per cent of small firms (having between 16 and 50 employees) and in about 85 per cent of medium sized firms. The focus of the survey was on the identification of the cost of the maintenance, revealing that it is almost 2 per cent of turnover, that there is no significant difference related to firm size, sector or maintenance policies. It was found that a fire-fighting attitude still prevails in many firms, about the 40% of the maintenance activities are reactive task carried out after a failure, this was also reported by Ferrari et al. (2002).

Another survey was carried out in 2002 by a regional section of the AISL, the Italian Society for Work Studies (Ghirardo, 2004), this one was more on local


27

scale, involving 62 medium firms, and confirmed the percentage of reactive maintenance and the same attitude towards maintenance. It was found out that only the 20 per cent of the examined companies calculate and took into account inefficiency costs (costs of loss of asset availability), caused for example by stops due to reactive or delayed maintenance.

There are also cases of excellence and implementation of the best maintenance policies testified by single case studies (Ferrari et al., 2002) or collections of case studies (Cigolini and Turco, 1997) presented in international literature. It is worth noticing that most surveyed cases regard either large industries or some smaller manufacturing plants that belong to large trusts or multinational groups.

Regarding the structure, we can observe that the internal maintenance structure is usually quite small (about 70 per cent of firms have up to five maintenance operators and about 60 per cent of firms have a spare part inventory value of less than 50,000€). In most cases when needed the internal capacity is assisted by external support. Vertical integration in maintenance apparently is present in only the 6 per cent of firms, who declare to be completely self-sufficient. The majority of firms primarily use the most basic form of maintenance contract, the work package contracts. This contract is task oriented and does not allow the firm to take all the benefits of the maintenance outsourcing, as shown by Tsang (2002) because occasional service supplier normally try to minimize their investments in staff development, equipment and new technologies. The more advantageous and complex performance-contracting mode is selected by only the 15 per cent of firms as main option.

Larger firms are also more capable of adopting more advanced forms of contracting out maintenance (performance contracting is the main option in 31 per cent of large firms against 10 per cent of medium firms and 6 per cent of small ones).

Regarding the technologies, CM is widely adopted, it is present in the 52 per cent of firms, but the diffusion of CMMSs is really limited, it is present in only the 35 per cent of the companies, a result that is comparable to the diffusion in other countries more than 10 years ago, see Ikwan and Burney (1994), Jonsson (1997), Swanson (1997).

The diffusion of CM is not statistically related to the size of the company (it is adopted by 39 per cent of small firms, 60 per cent of medium ones and 55 per cent of large ones) but it grows significantly with operation time (CM is present in about 39 per cent of firms operating on a single shift basis, but in more than 65 per cent of firms operating on a two or three shift basis).

The prevalent usage of CMMSs is for data recording and preventive maintenance planning, while more complex activities involving elaboration of data are seldom


28

performed (e.g. maintenance budgeting).

The presence of CMMSs is directly related with firm size: CMMSs exist in 29 percent of small firms, in 37 per cent of medium ones and in 41 per cent of large ones.

Concerning CMMS presence, it is worth noticing that the way in which the CMMS is used has an important impact on performance. In particular, a more frequent usage for PM planning is associated to a better safety performance, while a more intense usage for spare parts management and maintenance budgeting is significantly linked to a stronger contribution to lower production costs. Thus, the point is apparently in using a CMMS rather than in having a CMMS.

As to organization, a decentralized maintenance department depending on production functions represents the prevailing structure (54 per cent of firms).

In 57 per cent of firms, the head of this department (or of the maintenance function) is a skilled worker and only in the remaining 43 per cent of firms he belongs to middle or senior management.

In small firm is more common to delegate to the operator some maintenance task (77 per cent of small firms, against 47 per cent of medium and 36 per cent of large ones), while a centralized technical department is more common in large enterprises (44 per cent) and in medium enterprises (24 per cent) than in small ones (16 per cent), and a combination of centralized function integrated into production is more common in medium firms (29 per cent) than in small (7 per cent) or large ones (20 per cent).

Regarding the maintenance planning and control the formalization is limited, the maintenance orders are all written only in 35 per cent of firms, a spare parts stock book exists in just 39 per cent of firms and only a minority of companies (11 per cent) has monthly budgeting.

Compared with previous Italian studies (Ghirardo, 2004), a positive sign is the growing awareness about the inefficiency costs, which are taken into account in 48 per cent of firms, even if in a rough way.

Concerning maintenance policies and concepts, there is a limited diffusion of TPM which is present in 16 per cent of the organizations and a reactive maintenance proportion of about 55 per cent, which is well above recommended values of 30-40 per cent (see, for example, Jonsson (1997)). The result is similar to those reported in older surveys concerning Italy (Ferrari et al. (2002) or Ghirardo (2004)) and other countries (Jonsson (1997)). Although widespread, CBM has a limited weight among normal maintenance policies (about 10 per cent of total maintenance).


29

Among preventive maintenance approaches, condition based maintenance demonstrates to be extremely effective, being positively correlated with better cost, quality and safety performance. This practice appears to be easily available to small and medium firms that can use it to improve their performance.

Also the TPM has proved advantageous, but mostly in terms of quality, and safety without a clear correlation with cost reduction, the same result can be found in other studies, e.g. by McKone et al. (2001), who hypothesize that “TPM allows for effective use of the budgeted maintenance expenses and is able to improve inventory turns, quality, and delivery while maintaining stable production costs”. Also for this maintenance concept applicability and effectiveness do not depend on firm size.

Finally, the performance scores are generally unrelated to the firm size. The only weakly correlation is between yearly turnover and contribution to availability improvement, the smaller the firm, the better the perceived performance.

There are minimal differences in the maintenance strategies in different sectors . Significant ones concern organization (with a prevalence of dependence on production and transfer of maintenance tasks to production in the metal working sector, of centralized maintenance departments in process industry and other industries and of a combination of both structures in the wood working sector) and the diffusion of TPM, which is concentrated in the metal working and machine manufacturing industries (it involves 31 per cent of the firms of this sector but only 7-8 per cent of firms of other sectors).

The general picture evidences some criticalities, such as too much fire-fighting and limited preventive approaches, and, particularly in small firms, low status of maintenance management as to internal capacity, retribution and education of persons in charge and inadequate diffusion and use of planning and control tools, especially CMMSs. Most of these critical issues were nevertheless pointed out by research on manufacturing firms in several western countries

There are also some strong points, including the long experience of most maintenance heads, the growing awareness of inefficiency costs and the increasing diffusion of condition based maintenance and of TPM across all industries, independently of size. The average performances are generally more optimistic than similar measurements presented in literature (see Swanson, 2001), the size of our sample (100 firms) and the numerous and consistent correlations between maintenance best practices and performances highlighted in this study support our trust that these scores describe correctly, if qualitatively, the obtained maintenance results.


30

As to performance, an interesting finding is that there is no direct correlation between firms’ size and maintenance performance. Good results are equivalently reported by small and large firms.

It is possible to observe that maintenance visions and strategies influence maintenance results significantly, the best performances are achieved thanks to the use of maintenance policies and TPM programs. In particular, a practical implication is the confirmation that CBM can contribute to improve performance and thanks to the fact that these technologies are becoming more widespread and cheaper this practice can be easily adopted even by small firms, leading them to optimize their maintenance results.

Finally, the analysis of data from the examined area confirms the general indication that the usage of preventive maintenance, including CBM, should be extended. In any case this could be done only with the acquisition of opportune maintenance engineering instruments to guarantee that the preventive maintenance programs can harmonize well with production schedules and with the actual state of manufacturing equipment. This requires a more extensive use of the CMMSs and to use these instruments at their full potential it is necessary to train adequately the human resources: maintenance personnel could thus be empowered, waste of resources could be avoided and a synergic positive effect could add up to the extension of proactive maintenance practices.

2.3 North America The data are taken from the Aberdeen report (2006), there is a big difference in the size of the firms between the italian survey and this one, in fact this survey is based on 43% of respondents from large enterprises (annual revenues above US$1 billion); 27% from mid-sized enterprises (annual revenues between $50 million and $1 billion); and 30% of respondents from small businesses (annual revenues of $50 million or less).

The companies are classified in 3 categories according to performances achieved in the asset management: best in class (those who have mature asset management strategies and operations), industry average (companies that have implemented formalized asset management programs in some areas), and laggard (those companies that are just embarking on asset management and/or are meeting with some resistance).

The use of preventive maintenance is diffused between the 44% of the firms and the RCM is present in the 42% of the companies and the TPM in the 31%.

The CMMS is present in the 72% of the companies but in the 64% it is not completely integrated in real time shop floor system and in the remaining 8% of


31

the cases is present a fully automated and holistic management system to support the tactical and strategic decisions.

The average of the outsourcing service utilization is the 59% but it has a peak for the best in class companies that utilize the third part services in 85% of the firms.

The use of asset performance management is present in the 73% of the best in class firm and in the 47% of the other companies.

2.4 Sweden The information are taken from the Maintenance practices in Swedish industries: Survey results by Imad Alsyouf (2007). This survey is based on a sample of 185 firms that employ from 37 to 2400 people and a turnover from 100k€ to 1 billion €.

The average number of maintenance employee is 32 and the 66% of them have more than 10 years of experience in the work.

The average budget for the maintenance is the 4% and the 20% of this amount is used for the outsourcing of the services.

The Preventive maintenance either time or use based is the most used maintenance strategy followed by the CBM and the reactive maintenance and then there are TPM and RCM.

2.5 Comparison

The analysis of the survey show that the use of the maintenance policies and programs have a positive effect on the performances of the company but even if the advantages are clear the use of these technologies is limited. The use of these practices is more common in the medium and big firms (41% compared to the 29% of the small companies), this can be correlated to their possibility to spend more money than the small companies because the adoption of these technologies is expensive and the results are visible only years after their adoption.

In the Italian situation with the high number of small or micro firms (94% of the total number of companies has less then 20 employees), the presence of few or no maintenance personnel and a management that is not informed about the best practices the usage of these technologies is less than the other countries.

The main reason of the scarce diffusion of the best maintenance practices is the cost, these technologies are expensive and return of the investment is not guaranteed, if they are correctly applied then this cost will be repaid but it is


32

difficult to calculate how much time will it take and the risk of not being able to utilize them in the correct way is high.


33


ATION

YEAR

WORK

CONTRIBUTION

Maintenance management in italian manufacturing firms

Damiana Chinese, Gianni Ghirardo

Journal of Quality in Maintenance Engineering Vol. 16 No. 2, 2010 pp. 156-180

2010 Survey

Maintenance in SMEs in Italy

Cattaneo, M.

AIMAN, Milan

2000 Survey

TPM: situation and procedure for a soft introduction in Italian factories

Ferrari, E., Pareschi, A., Persona, A. and Regattieri, A.

The TQM Magazine, Vol. 14 No. 6, pp. 350-8.

2002 Survey

Maintenance in Pordenone province

Ghirardo, G.

Manutenzione Tecnica e Management, February, pp. 41-4, available at: www.manutenzione-online.com (in Italian)

2004 Survey

Total productive maintenance practices: a survey in Italy

Cigolini, R. and Turco, F.

Journal of Quality in Maintenance Engineering, Vol. 3 No. 4, pp. 259-72.

1997 Survey

Strategic dimensions of maintenance management

Tsang, A.H.C.

Journal of Quality in Maintenance Engineering, Vol. 8 No. 1, pp. 7-39


Strategic dimensions of maintenance management

Tsang, A.H.C.


2002 Survey


34

Maintenance in Saudi industry

Ikwan, M.A.H. and Burney, F.A.

International Journal of Operations & Production Management, Vol. 14 No. 7, pp. 70-80.

1994 Survey

status of maintenance management in Swedish manufacturing firms

Jonsson, P.


1997 Survey

An empirical study of the relationship between production technology and maintenance management

Swanson, L.

International Journal of Production Economics, Vol. 53

1997 Survey

The impact of total productive maintenance practices on manufacturing performance

McKone, K.E., Schroeder, R.G. and Cua, K.O.

Journal of Operations Management, Vol. 19 No. 1, pp. 39-58.


Linking maintenance strategies to performance

Swanson, L.

International Journal of Production Economics, Vol. 70 No. 3, pp. 237-44.No. 1, pp. 39-58.


The Asset Management Benchmark Report

Jane Biddle

Aberdeen report

2006 Survey

Maintenance practices in Swedish industries: Survey results

Imad Alsyouf

Int. J. Production Economics 121 (2009) 212–223

2007 Survey


35

Cap. 3 Condition-based maintenance

This chapter describes one of the most promising maintenance policies that can help companies to improve their performances, reducing the costs and increase the availability. Although there are several advantages this policy it is not widely used as demonstrated by the surveys showed in the previous chapter. This issue is further investigated by the industrial case presented at the end of this work.

3.1 Definition The Condition Based Maintenance (CBM) is a maintenance policy that lies its basis on the Condition Monitoring (CM), the important parameters of an equipment are acquired and monitored either in an automatic or manual way. The CBM uses the CM to trigger the required maintenance tasks when they are really needed. If the value of a parameter is out of a bounds of a defined threshold then an associated task is triggered.

CBM has been defined as “Maintenance actions based on actual condition (objective evidence of need) obtained from in-situ, non-invasive tests, operating and condition measurement.”

Another commonly agreed definition of CBM is (Jardine et al., 2006): “CBM is a maintenance program that recommends maintenance actions based on the information collected through condition monitoring (CM). CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behaviors of a physical asset. A CBM program, if properly established and effectively implemented, can significantly reduce the maintenance cost by reducing the number of unnecessary scheduled preventive maintenance operations”.

CBM is also defined as: preventive maintenance based on performance and/or parameter monitoring and the subsequent actions (EN 13306).

The main point is to assess the condition of the equipment during its normal operation utilizing the data acquired through sensors or the measurement chains and monitor its behavior. If the condition of a component is degrading through the time, like in the P-F curve of the figure 4, it is possible identify its degradation and exchange it before the failure.


36

Figure 4: P-F curve

The degrading of a component is starting from the normal condition and will end at the failure point F. It is possible to identify a point P that can be used as a threshold to identify the incoming failure. These information are collected and analyzed to recognize whether it is necessary to carry out any maintenance task or not and decide the best time to execute the maintenance to avoid breakdowns or malfunctions. The degree of automation in this process can vary from human visual inspection to a fully automated system based on the sensor reading, data manipulation, condition monitoring, diagnosis and prognosis.

In recent years the CBM has received an increasing attention from the industry due to the improvements in the reliability of the techniques available for the prognosis and diagnosis but also for the developments in the ICT solution to allow the communication between the components of the CBM process chain.

CBM is also one of the most important research topics of maintenance.

CBM aim is to avoid or at least to reduce the failures and the unnecessary maintenance actions on an equipment. Avoiding the breakdowns has a great economic impact thus, in some situations, important savings can be achieved by the use of this policy. According to Mobley (2002) in particular situations and when the technology is properly used to gain maximum benefits, a successful predictive maintenance program should generate a return on investment of 10 and 12 to one, that means that the plant can save 10 or 12€ for each euro invested to deploy the CBM approach.


37

3.2 Condition monitoring The Condition Monitoring (CM) can be classified in two ways according to the interval between two subsequent acquisitions of a variable: continuous and periodic.

In the continuous CM the status of the machine is checked continuously, the signals from the sensors are collected and interpreted to individuate the equipment condition. An alarm is triggered whenever the value read from the sensor is different from the normal, this is usually the indicator of degradation or failure. There are two big limitations for the continuous CM, the first one is the cost, the continuous acquisition requires a more powerful hardware and big storage capacity, that is often expensive and the second one is that when the signal acquired is noisy then the results of the diagnostic are not always reliable because of the noise added to the signal that can hide the fault of generate false alarms (Jardine et al., 2006).

Periodic CM is more widely used because it is more cost effective and typically is more accurate because it uses filtered and/or processed data. The risk of a periodic monitoring is that it is possible to miss some failure events if they happen in the interval between two checks or to recognize the failure when it is too late. One of the most important points is the determination of the correct time interval between the checks, this argument has been widely studied to try to find the optimum compromise between the cost and the capability to identify the failures. No preventive maintenance would lead to breakdowns which may affect production, and inflict money losses on the firm, an interval too short would lead to unnecessary prevention costs due to the cost associated to the acquisition of the data, ,on the opposite too long intervals would result in both inconveniences, as they will involve preventive maintenance actions and would lead to uncontrolled breakdowns. The optimal solution is to change the interval dynamically over the equipment life having short intervals on the first phase of the life of the component, longer intervals during the normal operation period and intervals increasingly shorter the more the part shows sign of degradation.

3.3 CBM steps Lee et al. (2004) has identified three key steps for a CBM program. The main idea of CBM is to utilize the information about the health of an equipment identified to minimize the system downtime and balancing the risk of failure and maintenance costs. The decision making in CBM recommends efficient maintenance policies focusing on prediction, and to do so, many diagnostic tools and methods have been developed with much more success. The three steps are (Lee et al. 2004):


38

• Data Acquisition: data from the monitored equipments are collected and saved.

• Data Processing: the data collected are cleaned and analyzed, specific analysis tools are used according to the type of data.

• Maintenance Decision Making: after the processing of the data these information are used to decide which maintenance actions have to be taken. Diagnostics and prognostics are two important activities in this stage. Diagnostics is the identification of the nature of a fault; machine fault diagnostics is a procedure to link the measurement or typical feature to the failure, this procedure it is usually done manually, with the support of some auxiliary tools (Jardine et al, 2006) but there are also some automatic diagnostics systems available, most of them exploiting artificial intelligence and neural networks.

3.5 Advantages and weak points

The main advantage of the CBM is the possibility to obtain a significant cost reductions and plant availability improvements; furthermore, it brings to Furlanetto et al. (2006):

• A reduction of component replacing and so a reduction of “early failures” (in cases of bathtub curve validity) ensuring a better maintenance quality;

• The possibility to acquire a deeper knowledge of the equipment behavior, thanks to a more careful analysis of weak signals (A weak signal can be defined as a signal that can be only caught by instruments);

• A better personnel management (as in all preventive maintenance policies); preventive maintenance allows the planning of interventions, and this can be subject to personnel employment optimization: tasks can be organized in order to level the workload and thus obtain a reduction of the number of maintainers needed to manage demand peaks and to better utilization of the personnel.

The main disadvantages of this policy are (Furlanetto et al, 2006):

• The policy is useless if the faults are random and without any identifiable signal of degradation to anticipate them.

• There are some interventions that cannot be postponed because of standards in Health, Safety and Environment (HSE), that define the interval between two maintenance tasks, so not always is possible to use the best possible schedule.

• A series of technologies, methods and techniques must be introduced, and


39

this requires some expenses, whose entity should be compared to the corresponding expected benefits. It is necessary some knowledge to use these instruments so also the cost to teach the user must be considered, this “knowledge cost” can be reduced if these activities are externalized.

• The expenses for the installation of the CBM can be quite large, especially the cost for the instrumentation in particular if the goal is to monitor equipment that is already installed. It is therefore important to decide whether the equipment is important enough to justify the investment.

• It is not always easy to achieve the desired accurate maintenance due to variables such as the complexity of the environment, the inner structure of the equipment, obscure failure mechanisms, etc.

3.5 Prognostic While the diagnostic gives information about the actual state of a component, like healthy, degraded or faulty, the prognostic goes forward and use the data acquired to try to forecast when it will fail. There are two important quantities that can be estimated: the remaining useful life (RUL), and the risk for one or more failures during a defined period of time (typically, the time to next inspection); in both cases, this information is calculated on the basis of the current machine condition and the past operation profile. So, by a series of instruments and techniques, data coming from the plant are captured and used for decision making, as shown in Figure 5.

.

Figure 5: diagnostics and prognostics are based on multi-source data (VTT, 2006)


40

The prognostic is a very promising feature of the CBM because knowing the RUL of a component can allow the maintenance manager to schedule the exchange or the repair of the part at the best time and exploit the component at the maximum exchanging it only when its residual life is nearly zero.

The various process of the prognostic are shown on figure 6, it is possible to identify the CBM steps in the layers 3, 4 and 5.

Figure 6: Layers for the identification of a fault

3.6 Fault prediction methods The methods of fault prediction can be divided into three categories:

1) Fault Prediction Based on Experiences: this solution can be the only one applicable if a physical model of the equipment is not available or if there an insufficient sensor network to be able to identify the fault. The possible failures and the RUL of the equipment can be derived from the statistical data of similar equipment under the same operation conditions. This kind of predictions is usually very imprecise but nevertheless nowadays some maintenance managers still define the maintenance intervals on the basis of their experience.

2) Data-driven Fault Prognosis: the objective of this method is the identification of the future failures using the measured data, this require complex mathematical models of the machines. There are several ways to correlate the data from the


41

sensors and the future failures, Z. Dong et al. (2001) proposed a novel prediction method of mechanical equipment condition based on gray-theory. Wang et al.(2004) used a neuro-fuzzy system for the prognosis of machine health condition. H. Lu et al. (2001) adapted a time-series analysis to calculate mean performance prediction.

3) Model-based Fault Prognosis: this method requires an exact mathematical model of the equipment, this method can be utilized for the fault prognosis in two ways. One is to analyze the future parameters data that have been predicted by the model of the equipment. The second one is to use the result of the consistency check between the measurement data of the real system and the outputs of the mathematical model, in case of fault the difference between the real and the calculated value is big while during the normal operation the difference is only due to the normal noise and modeling errors. Although it is difficult to create a model for a complex equipment, S. Luo et al. (2003) show that thanks to recent advances in the model based design there is an opportunity to develop model-based fault prognostics.

3.7 CBM tasks scheduling In a competitive environment like the industrial one the maintenance is a trade-off between cost and risk, the decision about which tasks as to be carried out, their scheduling and the allocation of the resources has to be made upon up-to-date information. It is difficult to take the optimal decision because normally the required information are not easily available or merged.

To take the best decision information about the state and health of the machine, the cost of the maintenance tasks, the cost of the loss of production and other non-technical data like the customer information. All these information are usually scattered between different systems or they have a different units or time scale.

The decisions about the maintenance activities have to consider costs, criticalities and the available resources to define the priorities of the tasks. The costs to consider include all the cost due to:

• Production loss: Production loss will be calculated by determining the reduction in production rate for the asset due to a failure and multiplying this by the value that would have been added in the process. However, the value added to a product by an asset will change in many organizations depending on what product is being produced at that particular point in time. Consequently the system must have access to the production schedule to determine an accurate figure.

• Capital loss: Capital loss is the sum cost of labour, spare parts and any secondary damage that would be caused to other assets in the event of


42

a failure.

• Quality loss: Quality loss is the estimated cost of a reduction in quality, reworking or scrap due to an asset not meeting specified tolerance levels or any other quality related issues.

• Safety and environment: Safety and environment costs are any fines or compensation claims that have been incurred as a direct result of an asset failure.

• Customer satisfaction: Customer satisfaction is the cost of lost orders or fines due to late orders as a result of asset failures.

Many Computerised Maintenance Management Systems (CMMS) use condition monitoring to identify a faulty condition of the machine and define the maintenance activities. There is a great deal of literature which concentrates on modelling for fault diagnosis and location, but there is less which deals with decision-making in maintenance management.

Jardine’s method uses a Markov chain to represent the behaviour of a physical system, and combines a number of condition indicators, coupled with failure cost data and life expectancy. The factors are combined in the proportional hazard model. The future evolution of a Markov system is only dependent upon the present condition, unlike a regression method which predicts a dependent variable based upon the history of several independent variables (A.K.S. Jardine, 1998).

Sherwin described the application of Weibull analysis to extensive failure data to effect decision-making on maintenance in the process industries. The technique is appropriate for determination of the failure regime, and hence to modify maintenance policy. It is however somewhat reliant on having sufficient failure data to analyse, and does not attempt to prioritise individual events (D.J. Sherwin et al. 1980)

Al-Najjar assessed maintenance strategies using a fuzzy multiple criteria decision-making (MCDM) evaluation methodology, and showed how the most informative or efficient maintenance approach would lead to less planned replacements, reduced failures higher utilization of component life. The relationship with business objectives was discussed, but the emphasis was on strategy rather than individual events (B. Al-Najjar et al., 2003).

Wang applied a stochastic recursive control model to condition-based maintenance. Actions to be taken, and optimal condition monitoring intervals, were considered as different decisions. A stochastic recursive filtering model predicted residual life, and then a decision model recommended actions (W. Wang, 2003).

Al-Najjar proposed an all-encompassing approach called total quality maintenance, which combines the areas of production and maintenance to


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maximise competitiveness, and central to which is a common database. The concept recognised the problems that we have tried to tackle in this work, and hence there is certainly scope in the concept for ranking of potential CBM failures, but it is not explicitly described. It was acknowledged that plenty of real data would be required for testing.

The order of the maintenance activities must be decided according to:

• production schedule;

• equipment requirements;

• equipment condition;

• required quality level;

• required safety and environment legislation;

• available resources;

• priority of tasks.

These information are available from the following sources (Kofi, 2007)

• production schedules;

• condition monitoring systems;

• maintenance management systems;

• financial records;

• health and safety regulations.

At the best these information can be found looking across the servers to find the correct spreadsheet or database, in the worst condition it is necessary to ask to several people paper documents. This can be a very time consuming task. When the response must be quick, management may take the decision based on his instinct.

3.8 Common difficulties for the implementation of CBM

The implementation of condition based maintenance on new equipments is a complex task, especially if there are no similar machines installed in the plant.

There are no information available about which parts are critical, which parts should be controlled and how often the check should be carried out. Obviously the manufacturer of the equipment have an idea of which components require more


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maintenance thanks to the intervention carried out or to the spare part sold to other customers with similar machines but even the manufacturer does not know exactly all the faults because most of the small failures are repaired directly by the customer and not reported.

Another difficulty is that even if the sensors are installed and the components are monitored it is necessary some time to acquire enough data to be able to identify the failures, so it is necessary to wait until some faults have occurred and then analyze the data to identify the pattern.

To identify which component and asset will have the greatest effect on an operation if it will fail the criticality assessments procedures are used. There are numerous methods currently available to maintenance managers to assist them in targeting those assets that are most critical to the department. One of the most popular and widely used methods is ‘Failure Modes, Effects and Criticality Analysis’. FMECA is a bottom-up approach that ranks assets in order of priority by determining the consequences, probabilities, and in some cases, the likelihood of detecting asset failures.

On-line criticality is an important input to the process. Typical criticality analyses (FMECA, etc.) have been done, but remain on paper.

Every company has its own way to acquire and store the data, so the information are often not shareable and even if they are in the same format no company will give it away freely because they were expensively acquired and they can give and advantage to a competitor, for that reason if a company want to start to monitor an equipment has to start from scratch and that is the main reason why it is so expensive and complex.

After that critical components have been identified the setting a correct threshold value for the condition based fault identification is problematic, the main problems are:

• The setting of the alarm threshold at the initial phase has no historical data, so a heuristic approach is used, this relies on the expertise or from the experience on similar machines.

• Normally there is no adaptation of the threshold according to the actual condition e.g. load or speed.

• The manual review is laborious and is often not done.

The quantity of condition monitoring activity and the impossibility to set perfect alarms levels has led to a problem for maintenance personnel that have to deal with a great quantity of alarms on a daily basis. The human decision-maker must assume that the alarms are true until it is proved the contrary. The decision of which alarm has to be checked as first can be a difficult and time-consuming procedure that normally relies upon the experience of the operator.

Plant executives, maintenance managers and work planners have always wanted


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to have information about the condition of equipment assets immediately available when they need it. Unfortunately, this information is usually scattered among separate information systems making it difficult or impossible to view on one computer terminal and use as a basis for sound asset management decisions.

The integration in the information systems is also one of the problems.


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Reference CONTENTS REFERENCES PUBLICATI

ON

YEAR

WORK

CONTRiBUTION

A review on machinery diagnostics and prognostics implementing condition-based maintenance

Jardine, A., Lin, D. and D. Banjevic

Mechanical Systems and Signal Processing, 20 (7), pp. 1483-1510


An introduction to predictive maintenance Second Edition

R. Keith Mobley



An integrated platform for diagnostics, prognostics and maintenance optimization

Lee J., Abujamra R., Jardine A.K.S., Lin D., Banjevic D.

Proceedings of the IMS ’2004 International Conference on Advances in Maintenance and in Modeling, Simulation and Intelligent Monitoring of Degradations, Arles, France


Principi generali di gestione della manutenzione

Furlanetto L., Garetti M., Macchi M.

Franco Angeli, Milano, Italy.


Continuous-Time Predictive-Maintenance scheduling for a deteriorating system

Antoine Grall, Laurence Dieulle, Christophe Bérenguer, and Michel Roussignol

IEEE TRANSACTIONS ON RELIABILITY, VOL. 51, NO. 2 , 2002



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Condition based maintenance optimization by means of genetic algorithms and Monte Carlo simulation

Marseguerra, M., Zio, E. and Podofillini, L.

Reliability Engineering and System Safety, Vol. 77 No. 2, pp. 151-65


Closed-form analytical results for condition-based maintenance

Chen, D. and Trivedi, K.S.



Joint optimal periodic and conditional maintenance strategy

Jamali, M.A., Ait-Kadi, D., Cle´roux, R. and Artiba, A.

Journal of Quality in Maintenance Engineering, Vol. 11 No. 2, pp. 107-14.


Reliability prediction for condition-based maintained systems

Saranga, H. and Knezevic, J.



A condition based maintenance model for a two-unit series system

Barbera, F., Schneider, H. and Watson, E.

European Journal of Operational Research, Vol. 116 No. 2, pp. 281-90


Choice criteria in conditional preventive maintenance

Luce, S.

Mechanical Systems and

Signal Processing, Vol. 13 No. 1, pp. 163-8


Communication, Collaboration and Business Decision Making in Distributed Complex

Michael Hecker, Alankar Karol, Christopher Stanton, Mary-Anne Williams



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Environments Proceedings of the International Conference on Mobile Business (ICMB’05)

Advances in Smart Sensor Technology

Dr. Takoi K. Hamrita, Nivedita P. Kaluskar, Kurt L. Wolfe

IAS 2005


Smart Sensor Platform for Industrial Monitoring and Control

Harish Ramamurthy, B. S. Prabhu and Rajit Gadh, Asad M. Madni

IEEE


Sensors in wireless network system

Radimir Vrba, Ondrej Sajdl, Miroslav Sveda

IEEE


life cycle monitoring with EPC identified smart sensors

Tielin Shi, Jiangbin Zhao, Wuxing Lai, Guanglan Liao

IEEE


Plug-n-Play Smart Sensor Based on Web Service

Fabrizio Ciancetta, Biagio D’Apice, Daniele Gallo, Carmine Landi

IEEE SENSORS JOURNAL, VOL. 7, NO. 5


Wireless Industrial Monitoring and Control Using a Smart Sensor Platform

Harish Ramamurthy, B. S. Prabhu, Rajit Gadh, and Asad M. Madni

IEEE SENSORS JOURNAL, VOL. 7, NO. 5


Smart Sensor Design: The Art of Compensation and Cancellation

Kofi A.A. Makinwa, Michiel A.P. Pertijs, Jeroen C. v.d. Meer, Johan H. Huijsing

IEEE



49

Towards Networked Smart Digital Sensors: a Review

Abhisek Ukil

IEEE


Networked autonomous smart sensors and dynamic reconfigurable application development tool for online monitoring systems

Zhao Jiangbin, Wu Jiankang, Shi Tielin,Xuan Jianping

IEEE


An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities

W.J. Moore, A.G. Starr

Computers in Industry 57 pp. 595–606


Investigation of the application of failure data analysis to decision-making in maintenance of process plants-1. General and theoretical background

D.J. Sherwin, F.P. Lees

Proceedings of the Institution of Mechanical Engineers 194 pp. 301–307


Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making

B. Al-Najjar, I. Alsyouf

International Journal of Production Economics 84 pp. 85–100


A stochastic control model for on line condition based maintenance decision support

W. Wang

Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics, Part 6, vol. 6, pp. 370–374


Decision optimization model for condition-based maintenance

.K.S. Jardine, V. Makis, D. Banjevic, D. Braticevic, M. Ennis



50

Journal of Quality in Maintenance Engineering 4 (2) pp. 115–121

Programming abstraction and middleware for building controlsystems as networks of smart sensors and actuators

Sebastian Zug, Michael Schulze, André Dietrich, Jörg Kaiser

IEEE



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Cap. 4 Standards In this chapter the most important standards regarding maintenance issues are analyzed. The purpose of the use of technical standards is to reach the optimal technical and economical solution to recurrent problems, the aims of standardizing can be presented as to (Engström, 1995):

• Facilitate communication by creating distinct conceptions with definitions and terms.

• Secure compatibility and interoperability through restrictions of size and weight, dimensions and interfaces.

• Accomplish variety reduction through selection of size and weight, dimensions and designs.

• Facilitate flexibility through modularization.

• Standardize characteristics, functions, qualities and safety for products, processes, systems, and services.

• Specify distinct testability methods.

According to Brunsson (1998) the motivations and arguments for following standards can be presented in four topics:

• Standards are an effective instrument for transformation of information.

• Standards constitute a method for coordination.

• Standards mean simplifications.

• Standards usually constitute the best solution.

4.1 OSA-CBM The Open System Architecture for Condition Based Maintenance organization (OSA-CBM) have developed a de facto standard that groups the functions that are required for a CBM system, it contains information about sensors, diagnosis and prognosis methods and also the the way to present the asset condition and recommended maintenance actions. If accepted as a non-proprietary standard, it will result in a free market for CBM components; it will be easier to upgrade system components, there will be a broader supplier community, more rapid technology developments, and reduced prices [www.osacbm.org]. The OSA-CBM


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divides a CBM system in seven different layers (components), with a modular solution. The OSA-CBM standard includes more than the system architecture of a CBM system, for example it describes also the communication, though, for more information see Thurston (2001); [www.osacbm.org].

The following is a proposal of an architecture that consider some reference standards:

1. Sensor Module: The sensor module provides the system with digitized sensor or transducer data. The sensor module could be developed using the published standard IEEE Std 1451 (see 4.2 for more information and references).

2. Signal Processing Module: The signal processing module receives data from the sensors or transducers or other signal processors and performs signal transformations and feature extractions. Its output data includes digitally filtered sensor data, frequency spectra, virtual sensor signals, etc. There are several ways to process the signals such as filtering, spectrum analysis, multiresolution decomposition etc. (Wen et.al,, 2000).

3. Condition Monitoring Module: The condition monitoring module receives data from the sensor modules, the signal processing modules and other condition monitoring modules. It carries out a comparison between the actual real values and the expected ones; it should also be able to generate alarms if the values exceed the defined thresholds. The condition monitor could be developed using the published standard ISO 13373-1 (see 4.3 for more information and references).

4. Health Assessment Module: The health assessment module receives data from condition monitors and other health assessment modules. This module determines the degradation of the condition of the monitored equipment, or component. The module also generates a diagnostic record and suggests fault possibilities. The health assessment module could be developed using the published standard IEEE 1232 (see 4.4 for more information and references) and ISO 13373-1.

5. Prognostic Module: The prognostic module should predict the future condition of the equipment or component. The module should be able to acquire information from all the lower modules of the model.

6. Decision Support Module: The decision support module receives data from the health assessment module and the prognostic module. Its primary function is to calculate the recommended maintenance actions or alternatives suggestion about how to run the equipment or component.

7. Presentation Module: The presentation module receives data from all previous modules; the most important are the data from the health assessment, the prognostic, and the decision support module. The presentation module could be built into a regular machine interface.

When analyzing the OSA-CBM standard it clearly shows that some of the


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different modules can be standardized. Several module standards within the CBM system technology are available on the market and if used, development might become more directed and even somewhat simplified.

4.2 IEEE 1451 The IEEE Standard 1451 is a standard for smart transducer interface that can be used in sensors and actuators that has been developed since the middle of the 1990’s. The NIST (National Institute of Standards and Technology) worked together with the IEEE (Institute of Electrical and Electronics Engineers) to develop a standard to achieve, among other, easy installation and upgrading of sensors (Gilsinn & Lee, 2001). The IEEE Std 1451 is composed of four sub-standars according to Potter (2001) all complementary. These sub-standards can be used as together or indipendently. Benefits of the complete IEEE Std 1451 are presented by Conway et.al (2000):

• Self-identification of transducers

• Self-configuration

• Easier to maintain long term self-documentation

• Easier to upgrade and maintain transducers

• An increase in data and system reliability

• Allows for transducers to be calibrated remotely or even to calibrate themselves

For more information on the IEEE Std 1451 see (IEEE Std 1451.1-1999,2000) and (IEEE Std 1451.2-1997, 1998).

4.3 ISO 13373-1 The ISO 13373-1 is a standard that describes the condition monitoring and diagnostics of machines, it provides general guidelines for the measuring and the data collection functions with a focus on machine vibrations. The standard was developed to ensure consistency in measurement procedures and practices and contains recommendation of following topics (Ali et.al, 2003):

• Measurement methods

• Measurement parameters

• Transducer selection

• Transducer location

• Transducer attachment


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• Data collection

• Machine operating condition

• Vibration monitoring systems

• Interfaces with data-processing systems

• Continuous monitoring

• Periodic monitoring

For more information on ISO 13373-1:2002 see [ISO 13373-1:2002, 2002]

4.4 IEEE 1232 The IEEE 1232, The Artificial Intelligence Exchange and Service Tie to All Test Environment, AI-ESTATE, was developed by the Diagnostic and Maintenance Control (D&MC) subcommittee of IEEE SCC20. The purpose of the standards is to “…provide formal models of diagnostic information to ensure unambiguous access to an understanding of the information supporting system test and diagnosis” (IEEE Std 1232-2002, 2002). The goals of the IEEE Std 1232 are to:

• Incorporate domain specific terminology

• Facilitate portability of diagnostic knowledge

• Permit extensibility of diagnostic knowledge

• Enable the consistent exchange and integration of diagnostic capabilities

For more information on the IEEE Std 1232 see [IEEE Std 1232-2002, 2002]

4.5 MIMOSA MIMOSA, the Machine Information Management Open System Alliances, is a non-profit organization that has developed open rules for the exchange of information between plant and machinery maintenance information systems. The relationship-based platform is called the Open System Architecture for Enterprise Application Integration (OSA-EAI) and its core is the Common Relation Information Schema, CRIS. The CRIS enables the exchange of information about equipment diagnostic and prognostics. The specification, CRIS Version 2.2, is openly published at the MIMOSA website (www.mimosa.org) (Kahn, 2003). The typical information that will need to be handled, presented by Thurston & Lebold (2001):

• A description of the configuration of the system being monitored

• A list of specific assets being tracked


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• A description of system functions, failure modes, and failure mode effects

• A record of logged operational events

• A description of the monitoring system and characteristics of the monitoring components

• A record of sensor data

• Resources of describing signal processing algorithms and resulting output data

• A record of alarm limits and triggered alarms

• Resources describing degradation in a system as well as prognostics of system health trends

• A record of recommended actions

• A complete record of work request

4.6 ISO 17359 This standard contains some guidelines for the implementation of a condition monitoring program for machines. It contains also the references of the standards that can be used for this purpose. This is a general standard that can be applied to all the machines. The guidelines describe among the other things also where the sensor should be placed to achieve the best results in the identification of the fault. The measurement point should be identified uniquely and permanently labeled or marked. The factors to consider are:

• safety

• high sensitivity to change in fault condition

• reduced sensitivity to other influences

• repeatability of measurements

• attenuation or loss of signal

• accessibility

• environment

• costs

4.7 ISO 13379 This standard describes the generic steps for a diagnostic study, the steps are the following:


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• analyze the machine availability, maintainability and criticality with respect to the whole process;

• list the major components and their functions;

• analyse the failure modes and their causes as component faults;

• express the criticality, taking into account the gravity (safety, availability, maintenance costs, production quality) and the occurrence;

• decide accordingly which faults should be covered by diagnostics (“diagnosable”);

• analyse under which operating conditions the different faults can be best observed and define reference conditions;

• express the symptoms that can serve in assessing the condition of the machine, and that will be used for diagnostics;

• list the descriptors that will be used to evaluate (recognize) the different symptoms;

• identify the necessary measurements and transducers from which the descriptors will be derived or computed.

4.8 ISO 13380 This standard contains the information about the machine operating condition. It says that when it is possible the acquisition of the measurement of different parameters at the same time or under the same conditions. For variable duty or variable speed machines, it may be possible to achieve similar measurement conditions by varying speed, load or some other control parameters and then start the monitoring when the machine has reached a predetermined operating conditions or a desired point in the transients. These information are compared with the reference values in order to detect the changes. The analysis of the trend is useful to identify development of the faults.


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Cap. 5 Predictive maintenance techniques There are several causes for a machine fault, the most common ones are attributable to design limitations or inaccurate assembly or installation but there are also failures due to a particularly aggressive environment or heavy operating conditions.

A big variety of techniques can be (and should be) used as a part of a Condition-based maintenance program.

A comprehensive maintenance program must include a variety of techniques, among these the most used the vibration monitoring, oil analysis and thermography.

This chapter will provide a description of the principal techniques

5.1 Vibration monitoring

Vibration monitoring is the primary predictive maintenance tool, it can be used on a variety of electromechanical parts like pumps, fans and almost all the moving parts.

A machine is subject to several sources of vibration that means that it has a composite vibration profile.

Vibration data can be acquired through accelerometers and analyzed in a frequency domain to separate the various vibration components; in this way it is possible to individuate the abnormal behavior of a part.

There are different sensors available at the moment with different characteristics, every application is particular so there are sensors for low or high frequencies, high temperatures, specific application that are more or less standard like drills and so on, so the choice must be made according to what is necessary to monitor.

The vibrations of a machine are relatively easy to analyze for the components that operate at constant speed but the variable speed make it more complicated.

The most frequent faults found as a result of vibration surveys are:

• Misalignment

• Unbalance

• Resonance

• Bearings

• Looseness


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• Flow-related problems

• Electrical

• Bent Shaft

• Gear Mesh

Of all of these the first four are the cause of almost 90% of all faults reported; unbalance, misalignment, looseness and bearing failure.

The most common components that can be found in a wide variety of equipments are fans and bearings, in the following paragraph examples of these applications are presented.

5.1.1 Vibration monitoring for fans

A fan can be defined as any device that produces an air current by the movement of a surface. A fan impeller consists of a number of blades that are welded or riveted to the impeller’s shroud and are mounted on a shaft. Typically, there are two bearing that support the shaft and the rotation is given by a motor that is connected to the shaft directly or indirectly.

There can be four types of faults for a fan:

• Imbalance: impeller imbalance that can due to the manufacturing process, mounting error or operation and it produces a high level of vibration that can damage bearings or the other components.

• Bearing defects: defects in bearings usually are the result of the wear out.

• Shaft faults: shaft faults are due typically to misalignments or cracks

• Resonance: operating a fan within the range of its components’ natural frequencies can cause a high level of vibration in the impellers, causing serious damages.

The vibration signature can be used to identify machine operating condition, when a fault occurs there will be difference in the signature.

The vibration of a fan can be measured in two ways, it is possible to measure the relative displacement of the shaft in its bearing with the use of proximity probes or measure the absolute vibration of the bearing housing with an accelerometer.

5.1.2 Vibration monitoring for bearings Vibration monitoring of mechanical bearing frequencies is currently used to detect


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the presence of a fault condition. Some studies show that bearing problems account for over 40% of all machine failures. Over the past several decades, rolling-element (ball and roller) bearings have been utilized in many electric machines while sleeve (fluid-film) bearings are installed in only the largest industrial machines. In the case of induction motors, rolling element bearings are overwhelmingly used to provide rotor support.

In many situations, vibration monitoring methods are utilized to detect the presence of an incipient bearing failure.

As shown in the figure 7 the wearing out of the bearing is accompanied with an increment of the amplitude of the vibrations so identifying this condition is important to detect the occurrence of a fault.

Figure 7: Example of vibration analisys for bearings

Rolling-element bearings generally consist of two rings, an inner and outer, between which a set of balls or rollers rotate in raceways. Under normal operating conditions of balanced load and good alignment, fatigue failure begins with small fissures, located below the surfaces of the raceway and rolling elements, which gradually propagate to the surface generating detectable vibrations and increasing noise levels. Continued stressing causes fragments of the material to break loose producing a localized fatigue phenomena known as flaking or spalling. Once started, the affected area expands rapidly contaminating the lubrication and causing localized overloading over the entire circumference of the raceway.

Eventually the failure results in rough running of the bearing.

Installation problems are often caused by improperly forcing the bearing onto the shaft or in the housing. This produces physical damage in the form of brinelling or false brinelling of the raceways which leads to premature failure. Misalignment of the bearing, which occurs in the four ways depicted in figure 8, is also a common


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result of defective bearing installation.

The most common of these is caused by tilted races.

Regardless of the failure mechanism, defective rolling-element bearings generate mechanical vibrations at the rotational speeds of each component. These characteristic frequencies, which are related to the raceways and the balls or rollers, can be calculated from the bearing dimensions and the rotational speed of the machine. Mechanical vibration analysis techniques are commonly used to monitor these frequencies in order to determine the condition of the bearing.

Figure 8: Examples of mechanical problems of the bearings


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5.2 Thermography Thermography is a non destructive method to monitor the condition of plant machinery, structures and electrical equipment.

It involves the measurement or mapping of the surface temperature of an object through the use of instruments that are designed to monitor the emission of infrared energy.

By detecting thermal anomalies different problems can be detected such as screws not perfectly tightened in an electrical cabinet or a lack of lubrication in some part of the machine or an overload of some component, in the first case the increased resistance of the connection, in the second case the increase of the friction and in the third case the higher current absorption can generate more dissipation and so the temperature of the component increase.

Checking this and compare it with the normal temperature of the component can help to identify an incipient fault.

The most used technique for thermography is the infrared imaging, on the market are available different models of infrared camera, with different characteristics and there are different companies specialized in the thermographic analysis.

The thermographic analysis is normally carried out on time based if it is not important for the production process, for example the check of the electrical cabinet is carried out normally once every six or twelve months.

This method can identify the component that is likely to have an incipient failure but the information must be analyzed to understand the cause.

5.3 Oil analysis Two main techniques are related to the oil analysis, the lubricating oil analysis and the wear particle analysis.

In both cases the lubricating oil of the machine is sampled and analyzed on regular basis to check if it meets the requirements for the application and to get information about the wearing condition of the machine.

Normally these analyses are carried out by specialized external laboratories because they require the use of spectrographic instrumentations that are expensive.

Lubricating oil analysis should be limited to a proactive program to conserve and extend the useful life of lubricants.

This technology can not be used at the moment to identify a specific failure mode or root-cause of incipient problems.


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Primary applications of this analysis are quality control, reduction of oil inventories and determination of the most cost-effective interval for oil change.

Wear particle analysis provide direct information about the wearing condition of the machine through the analysis of the particle shape, composition, size and quantity.

The limitations of oil analysis in a condition-based maintenance programme are: high equipment costs, being a laboratory-based procedure, reliance on acquisition of accurate oil samples and skills needed for proper interpretation of data.

5.4 Pressure/temperature/current monitoring Temperature, pressure and current are normally parameters checked by the system or used in a control loop but these information can be used also to identify the degradation of the system or as a symptom of a problem.

For example the analysis of the time to reach a certain temperature in an oven can give an information that something is wrong, e.g. door not perfectly closed, air circulation problem heating element failure.

Analysing the absorbed current can be useful to check the status of motors, most common problems are the bearing problems or overloads that can be identified by the increase of the consumption.

Pressure checks give information about the status of the tubes, if there is a leakage the pressure will decrease or increase due to the sclerosis on the walls.

On a pump it is necessary to increase the speed to achieve the same pressure this is a symptom of wearing of parts.

Normally all the information are already acquired in the machine program, to use them for maintenance it is necessary to archive them and analyse the trend.

5.5 Visual inspection The visual inspection of the machine is the first and most used (and normally the most simple) method for predictive maintenance.

Maintenance technician performs daily checks on the critical parts of manufacturing system in order to identify potential failure or maintenance related problems.

Visual inspection is still a viable predictive maintenance tool and should be included in all maintenance plant programs.


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5.6 Noise Every equipment when it is working generate some noise, all the parts have a different sound and this sound can change if its working condition change.

Normally any industrial equipment is in a noisy environment but usually an operator is able to identify a change in the typical noise of the machine.

Lack of lubrication, loose screws or belt that has become loose for example can be detected by a change in typical sound.

This is not a automated tool but can be used as a preventive maintenance tool.

5.7 Weight check For system that have to dispense material one of the most used methods to check the status of the system is the weight check, after a defined number of parts or a defined quantity of material a weight check is carried out (automatically or by the operator).

The amount of material for a defined time should be inside a tolerance band, if the quantity of material is inside this window than the system can continue to work otherwise are necessary some adjustment to go on with the production.

Collecting these data and then analyse them can give an idea of the wear out of the parts and a trend can be identified.

5.8 Current analysis Up to now condition monitoring schemes for electrical motors have focused on looking for specific failure modes in one of three main components, the stator, the rotor, or the bearings. Thermal and vibration monitoring have been used for years but the attention in the most recent researches has been moved to the electrical monitoring of the machine with emphasis on inspecting the phase current of the machine.

In particular, a large amount of research has tried to use the spectrum of the stator current to identify rotor faults like mechanical unbalance or broken rotor bars. All of the presently available techniques require the user knowledge to distinguish a normal operation from potential failure mode. This is due to the fact that the monitored spectral components (either vibration or current) can be influenced by several sources including those related to normal operating conditions.

Many of these harmonics can be caused by ovalities in the rotor, voids in the


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casting, slot design, etc. For example, cyclically varying loads and mechanical unbalances can have the exact same effect on the motor current spectrum as a broken rotor bar or a bent shaft. For this reason an expert maintenance operator is usually necessary to analyze the data collected from online monitoring and decide if the measured spectrum is sufficiently different from the normal one to indicate the presence of a fault.

The trend in recent years is to try to use expert system or neural networks to automate the analysis of the measured spectrum to automate this procedure.

Neural networks can detects changes from a learned normal condition of the machine by recognizing pattern changes in the frequency components that have been selected by the expert system. The system must learn the spectral patterns specific to each component and it should monitor the spectrum looking for any change that can indicate a potential fault condition. This is an important development since it allows an immediate evaluation of the condition of the machine without requiring the presence of an operator.

The unsupervised on-line current monitoring system works in this way, first the sampler and preprocessor converts the time domain stator current signal into a frequency domain spectrum that can be analyzed by the computer. After this an expert system (rule-based) determines which frequencies should be monitored and then the neural network and the post-processor analyze these frequencies to check if significant changes are present compared to the normal condition to indicate a possible fault condition.

The transient behavior of a typical electrical load is strongly influenced by the physical task that the load performs. A load survey as shown that intrinsic properties modeled as nonlinearities in the constitutive laws of the elements that comprise a load, or in the state equations that describe a load, or both, create repeatably observable turn-on transient profiles suitable for identifying -specific load classes. This observation has led to the development of a transient event detector for non-intrusive load monitoring.

5.8.1 NILM The non-intrusive load monitor (NILM) determines the operating schedule of the major electrical loads in a building from measurements made solely at the utility service entry. For electric utilities and industrial facilities managers, the NILM is a convenient and economical means of acquiring accurate energy usage data with a minimal installation effort.

The step for the NILM are:

1) Data Acquisition: During this data acquisition step, the Digital Signal Processor (DSP) system collects a window of samples which will be searched for known transient patterns.


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2) Tree-Structured Decomposition: Once a full window or vector of samples has been acquired, the DSP system performs a tree-structured decomposition. In the current implementation, the input data for each coder step in the tree is computed before any pattern discrimination occurs at any scale step. A tree structure with a total of three 2 to 1 coder or scale steps proved sufficient for identifying all of the transients associated with the loads in the test stand described in the next section.

3) Set Scale Steps: Next, the DSP system searches at each scale for all of the transient types that could appear. There are three scale steps, first the finest sampled scale step is inspected, followed by the middle and coarse scales.

4) Initiate Pattern Search: A loop in the program flow at this point search for patterns over all three scale steps.

5) Hierarchical Pattern Search with V-Section Lock Out on Scale M: During each pass through the pattern search loop, the DSP system searches for the v-sections associated with the known transient events on a single scale. The pattern search is hierarchical, in that the DSP system searches first for the patterns with the most v-sections. When all of the v-sections for a pattern are found with both the shape and amplitude transversal filtering operations, the complete transient pattern is presumed to be present in the input data, and an event is recorded. A v-section lock out is performed at each scale. If a complex pattern is found in the input data, the location of the v-sections of the pattern are recorded. The identification of any subsequent, less complex patterns will not be permitted based on the detection of v-sections at the previously recorded, “locked out” locations.

6) Report Generation for Scale M: If all of the v-sections are found for a particular pattern, the transient pattern is presumed to be present in the data at the current scale M, and an event type and time is registered.

7) Decrement Scale Step: The scale counter M is decremented, and the pattern detection loop is repeated until all remaining coarser scales have been searched.

8) V-Section Lock Out Over All Scales: A final check is done to ensure that v-sections from a complex but coarse scale pattern were not used to match a less complicated, finer scale pattern.

9) Final Report Generation: A final report is generated of the type and time of occurrence of all positive event detections.

10) Standby: After reporting any contacts, the PC waits for the user to issue an arming command.

This was an example of the way in which a software for the current analisys is working.


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5.8.2 Electrical signature analysis Of the different approaches for the diagnostic of the faults for the industrial plants, the best solution is the one that it is not too much complicated and expensive but at the same time can effectively monitor the wear out of the components and prevent the failures. This is the goal of the electrical signature analysis, that use the information contained into the signal of the power supply to identify some features that can be associated to the state of the components. For each state it is possible to define an electrical signature that is characteristic for that functioning state. To identify the features it is necessary to analyze the information acquired by additional sensors or often it is enough to use signals that are already available. The second case is, when applicable, very interesting from the economic point of view.

The electrical signature identification is an extraction and identification of the features and the relative signatures problem, it is basically a classification task that is a problem that have been widely studied.

The typical engineering approach is based on the study of a mathematical model that can describe the system and identify the correlation between inputs and outputs. This approach is called white-box and it produces a model that requires few parameters that have a physical meaning and that can be calculated or measured from the system.

There are also other techniques that have been developed recently and that are based on statistical methods, soft computing or a combination of these methods. This approach is called black-box, the correlation between inputs and outputs is done with a non parametric model, it has a big number of parameters that have no direct physical meaning. The main advantage of these techniques is the capability to learn from data so the model parameters can be automatically configured without requiring a deep knowledge of the system. These methods are useful when the physical model of the system is not available or when it is too complex to be usable. The problem is the identification and calculation of the parameters instead of the definition of the model.

The ability to identify the behavior of the component depend from the quantity of data available for the various situation.

The electrical signature can be used either for the diagnostic of the system or for the prognostic.

The diagnostic is used to identify the presence of failures in the component, the features are extracted from the measured information and compared with the features of the normal operation to identify the faults. This process is a pattern recognition problem and the three most used approaches are the statistical, the soft


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computing and the model one.

The prognostic is the identification of the future failures, two methods are used up to now, the calculation of the residual useful life and the reliability approach. With the improvement of the techniques for the features extraction and the increasing of the sensitivity of the pattern recognition methods it is possible to identify the failure on an early stage and calculate the residual useful life of the component on the basis of the difference between the actual and the normal feature. Also in this case the statistical and soft computing methods are used.

5.8.2.1 Statistical model

In area of the statistical model there are several non parametric models that can be used for classification and regression problem, the most famous are the bayesian networks, the Hidden Markov Models, the decision trees and the Support Vector Machines.

The bayesian networks describe the probability distribution, the network is composed of several nodes on several levels, every node is a variable and the connections between the nodes describe the cause-and-effect link. The relation between inputs and outputs is decomposed in simple relation between variables. There are algorithms that can be used to identify the parameters to ensure that the network has the same behavior of the data.

The Hidden Markov Models (HMM) are dynamic bayesian model and they are usually used to identify temporal pattern. A HMM Markovian can be used to model a process where the state are unknown and the outputs are available.

The decision trees are used to model a process by steps, every node is a variable, every connection between the nodes is a value for that variable and the leaf nodes are the results of the functions.

The Support Vector Machines (SVMs) is a model that was originally developed for classification problems but it can be used also for regression. The problem is defined as an optimization problem that can be solved with the standard optimization techiniques.

5.8.2.2 Cluster analysis

One of the promising techniques to identify the features in the electrical signature is the cluster analysis, this method allow the group of the signal in different clusters according to their similar characteristics. The objective is to minimize the variance in a cluster and to maximize the variance between different groups, the result of the cluster analysis is a number of cluster that contain similar elements, according to the similarity between the actual feature and a cluster the state of the component is identified and also the residual useful time.


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5.8.2.3 Soft computing

The soft computing techniques are used to identify the correlation between the input and output signal of a system starting from the raw data (learning from data) without the necessity of a deep knowledge of the possible cases and the result is not affected by the uncertainty of the data. The goal is to define a function to correlate the inputs and the outputs, the parameters of the function are estimated from the data acquired during the functioning.

The mathematical model used in the soft computing is usually composed by several small units that execute a small task, a schema describe the way in which the units are interconnected and how their results are combined.

A specific algorithm uses the input data to configure the units to obtain the desired output given the inputs, this procedure is called learning or training. The complexity of the problem is the choice of the paradigm and its characteristics instead of the creation of a model like in the standard methods.

The soft computing paradigm can be classified as: neural networks, fuzzy systems, evolutive system and statistical methods. The different techniques can be used together in a hybrid method.

5.8.2.3.1 Neural networks

There are several type of neural networks, they have been studied in different fields. The learning of the neural network can be either supervised or not supervised and they can be used for regression and classification problems. The neural networks are a copy of the biological neural networks and they are composed of small units called artificial neurons that are programmed to execute a specific function.

The input fires of a certain set of neurons and an output is obtained the configuration of the network is done changing the parameters of the function of the neurons and their weight.

The most used model is the multistate feed-forward, the signal is going in one direction, from the input to the output, the neuron of a state are connected to all the neuron of the following state though a weighted connection, the configuration of the network has to change the weight of the connection and the parameter of the neurons to achieve an output similar to the real one given the same inputs.

5.8.2.3.2 Fuzzy systems

The fuzzy systems use the linguistic variable to create a model of the system, they are usually used to create a model starting from the knowledge from the experts.

The elaboration has two steps, the fuzzification and defuzzification, the fuzzification comprises the process of transforming crisp values into grades of


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membership for linguistic terms of fuzzy sets. The membership function is used to associate a grade to each linguistic term. In the second step the set is transformed to a crisp value, this transformation from a fuzzy set to a crisp number is called a defuzzification.

5.8.2.3.3 Evolutive techniques

The evolutive methods at every iteration calculate a set of possible solutions and the best solution according to the fitting value are combined together to obtain a new set of solution for the following iteration. The evolutive methods can be used for a wide range of problems, the simplest one is the optimization problem. The advantage of these techniques is that in the during the configuration phase are identified both the model and the inputs that are more important for the problem solution.

5.8.2.4 Feature selection

A common problem of the use of the soft computing the so called "curse of dimensionality", that refers to the fact that some problems become intractable as the number of the variables increases. In machine learning problems that involve learning a "state-of-nature" (maybe an infinite distribution) from a finite number of data samples in a high-dimensional feature space with each feature having a number of possible values, an enormous amount of training data is required to ensure that there are several samples with each combination of values. With a fixed number of training samples, the predictive power reduces as the dimensionality increases, and this is known as the Hughes effect or Hughes phenomenon (named after Gordon F. Hughes).

To avoid this problem the dimensionality of the problem is reduced selecting a set of parameters that represent the main characteristics of the input data, this procedure is called feature selection.

5.8.2.5 Performance estimation

To evaluate the performances of the soft computing model is to use a set of data that has not been used for the configuration. The available data must be divided into two parts, the first one is used to configure the model (training dataset) and the rest is used to validate the model (testing dataset). A more complex validation is made repeating several time the configuration and validation using randomly partitioned dataset.

If the model must be chosen during the configuration a set of data must be used for this task, this dataset is called valition dataset.


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The available data is divided keeping in mind that using more data for the training phase will produce a model that is describing well the system but it can be too adherent to the data and its noise (overfitting), on the opposite if few data are used in the training phase the model can be unable to reproduce correctly the behaviour of the system. For the testing dataset similar consideration can be applied, using few data will not consider all the possible condition and using too much data will subtract useful information for the configuration.

5.8.2.6 Electrical signature

The abovementioned methods can be used for the monitoring of the functioning state of a machine or an industrial plant, the monitoring is the check of the difference between the actual state and the standard conditions. Any change in the mechanical system attached to motor has an effect on its power requirement so monitoring the current consumption is possible to identify the presence of faults, so it is possible to identify an electrical signature for the normal operating condition, check the actual signature, compare it with the normal one and thanks to the methods described above classify the actual functioning state in normal, faulty or detect the early signal of failures.

5.8.3 Rotor analysis In Kim et al. proposed a simple automated technique for monitoring the rotor condition for voltage source inverter-fed induction machines at standstill. The main concept is to use the inverter for performing an offline test equivalent to the single-phase rotation test, whenever the motor is stopped. The motor is excited with a set of pulsating fields at a number of angular positions for observing the change in the impedance pattern for broken bar detection. The experiment has shows that broken bars can be detected with high reliability and sensitivity. The proposed technique can be programmed into an inverter without additional hardware as a built-in diagnostic feature to assess the rotor quality frequently whenever the motor is stopped. The new method has many benefits compared to the existing offline and online test methods that are used in the field. Unlike existing offline tests, the proposed method provides frequent and automated rotor condition assessment without additional test equipment or hardware. In addition, motor disassembly, manual rotor rotation, or rotor locking are not required for testing. This makes remote monitoring possible, which is advantageous for cases where the motor is operated under hostile ambient environments. Compared to online monitoring techniques, it is capable of providing a more reliable assessment of rotor bar condition since it is independent of motor operating conditions, such as rapidly varying frequency or load applications, or low slip


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operation, as it is a standstill test. It is also not influenced by coupling or load problems and does not require the rotor speed or motor/controller parameter information. This is a significant improvement in maintenance strategy since it is a convenient method that provides reliable assessment and helps save inspection cost and allows maintenance to be performed in a more efficient manner. The proposed method cannot provide continuous online monitoring, but it is sufficient to monitor the rotor condition whenever the motor is stopped since rotor faults are not rapidly progressing faults that require immediate motor shutdown upon fault detection. The proposed method can also be developed as a stand-alone offline test equipment for machine inspection in a machine shop or field or for post-manufacturing quality assurance testing at a manufacturing facility. It can also be used for offline verification of rotor fault alarms given by online monitors to prevent outage due to false alarms.

5.9 Process parameters check Information about the status of a component can be extracted from the signals acquired from the sensors during the operation of the machine. A signal taken by itself can be used for checking an alarm condition but if it is combined with other inputs coming from the machine it is possible to identify the status of a component.

For example if we have a pump actuated by a motor with its frequency converter and a pressure sensor, the controller will adjust the speed of the motor to keep the pressure as similar as possible to the desired value. The pressure sensor is used as a feedback and to control that the pressure remains inside the minimum and maximum tolerated thresholds. The control system will increase or decrease the motor speed according to the necessity. With the same information it is possible to achieve more than just a control, in fact it is possible to determine the status of the pump if the information about the speed and the pressure are correlated. When the pump is new there are limited leakages in the pump so the pressure is achieved with a low speed, the more the pump is wear out the more the leakages are consistent and the more the speed must be higher to keep the pressure to a desired level. So the correlation between the pump speed and the pressure give the information about the wear out of the pump without the requirement of any other input information and this can be useful to estimate the residual useful life and inform the operator when a threshold is exceeded.

The use of process parameter for the preventive maintenance has the advantage that it uses data that are already available in the system to extrapolate new information so it does not require any supplementary sensor and it has a reduced cost.


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ATION

YEAR

WORK

CONTRIBUTION

Transient event detection in spectral envelope estimates for nonintrusive load monitoring

Steven B. Leeb, Steven R. Shaw, James L. Kirtley, Jr.,

IEEE Transactions on Power Delivery, Vol. 10. No. 3, July 1995


Motor bearing damage detection using stator current monitoring

Randy R. Schoen, Thomas G. Habetler,

Farrukh Kamran, and Robert G. Bartheld

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 31, NO 6, NOVEMBERDECEMBER 1995


An unsupervided, on-line system for induction motor fault detection using stator current monitoring

Randy R. Schoen, Brian K. Lin, Thomas G. Habetler, Jay H. Schlag, and Samir Farag,

IEEE TRANSAmONS ON INDUSTRY APPLICATIONS, VOL. 31, NO. 6, NOVEMBEWDECEMBER 1995


An introduction to predictive maintenance Second Edition

R. Keith Mobley


2002 Spcific knowledge

Integrated Microsensor Module for a Smart Bearing with On-line Fault Detection Capabilities

B. T. Holm-Hansen and R. X. Gao

IEEE Instrumentation and Measurement Technology Conference Ottawa, Canada



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Fault detection and diagnostics for non-intrusive monitoring using motor harmonics

Uzoma A. Orji, Zachary Remscrim, Christopher Laughman, Steven B. Leeb, Warit Wichakool, Christopher Schantz, Robert Cox, James Paris, James L. Kirtley, Jr., Les K. Norford

IEEE


Field-based systems and advanced diagnostics

John Hartley

IEEE


Servicing industrial machinery

Nicolaie L. Fantana, Till Riedel

IEEE


Online and manual (offline) vibration monitoring of equipment for relilability centered maintenance

Sheetalnath Mahalungkar, Mike Ingrarn

IEEE


Condition monitoring of industrial fans

S.H.Ghafari

Proceedings 22nd Seminar on Machinery Vibration, CMVA 2004, October 27-29, Ottawa


Modelling of low shaft speed bearing faults for condition monitoring

Wang and Kootsooks

Modelling of low shaft speed bearing faults for condition monitoring , Mechanical Systems and Signal Processing


Transient event detection in spectral envelope estimates for nonintrusive load monitoring

Steven B. Leeb, Steven R. Shaw, James L. Kirtley, Jr.

IEEE Transactions on Power Delivery, Vol. 10. No. 3


An unsupervided, on-line system for induction motor fault detection using stator

Randy R. Schoen, Brian K. Lin, Thomas G. Habetler, Jay H. Schlag, and Samir Farag

IEEE TRANSACTIONS ON



74

current monitoring INDUSTRY APPLICATIONS, VOL. 31, NO. 6

Early defect identification: apllication of statistical process control method

Wenbin Wang, Wenjuan Zhang



A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machines

Jawad Raza, Jayantha P. Liyanage, Hassan Al Atat and Jay Lee



Automated Detection of Rotor Faults for Inverter-Fed Induction Machines Under Standstill Conditions

Byunghwan Kim, Kwanghwan Lee, Jinkyu Yang, Sang Bin Lee, Ernesto J. Wiedenbrug and Manoj R. Shah

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 47, NO. 1


Early Estimation of Faults in Induction Motors Using Symbolic Dynamic-Based Analysis of Stator Current Samples

R. A. Gupta, A. K. Wadhwani, and S. R. Kapoor

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 1



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Cap. 6 Smart sensors This chapter describes the smart sensors, explaining their characteristics, their advantages and disadvantages.

6.1 Description The term “Smart Sensor” was coniated in the '80s, when the first model of this sensors were born.

They are the evolution of the traditional sensors, according to the IEEE definition a smart sensor is:

“A sensor that provides functions beyond those necessary for generating a correct representation of a sensed or controlled quantity. This function typically simplifies the integration of the transducer into applications in a networked environment”

Another definition of “smart sensors” was given by the product manager of Honeywell Industrial Measurement and Control (Control Engineering Website), he defines them as:

“Sensors and instrument packages that are microprocessor driven and include features such as communication capability and on-board diagnostics that provide information to a monitoring system and/or operator to increase operational efficiency and reduce maintenance costs".

Smart sensors embed a local intelligence, they are driven by a microprocessor and they are able to receive and transmit data or commands on digital channel, analyze their status and report faults.

Several sectors of industrial applications (domotic, automotive, robotics, etc) require a great quantity of sensor at the lowest cost possible and adapted to the market constraints about safety, reliability and economy.

Detection and characterization of anomalies in an industrial plant provide improved plant availability and plant efficiency thus yielding increased economic efficiency. Traditionally, detection of process anomalies is done at a high-level control system through various signal validation methods. These signal validation techniques rely on data from transmitters, which measure related process variables. Correlating these signals and deducing anomalies often is a very time consuming and a difficult task. Delays in detecting these anomalies can be costly during plant operation.

Conventional centralized approaches also suffer from their dependence on detailed mathematical models of the processes. Smart field devices have the advantage of providing the necessary information directly to the control system as


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anomalies develop during operation of the processes enabling operators to take necessary steps to either prevent an unnecessary shut down before the problem becomes serious or schedule maintenance on the problematic loop.

Elimination of these unnecessary inspections can have a major impact on maintenance costs. Knowing that the instrument is healthy is therefore equally as important as if it has failed.

Maintenance log data of a large chemical plant have also shown that nearly 65% of the time these inspections indicated that transmitters were healthy, thus leading to waste of resources which increases the operational cost of a plant (John Hartley, Emerson Process Management).

6.2 Functionality To implement the smart sensor functionality we can identify three main parts:

• Signal processing

• Control and digital manipulation

• Communication and bus interaction

6.2.1 Signal processing The sensor produces an electrical signal that is proportional to the physical parameter that is being measured.

The signals acquired by the sensors are often very low of amplitude and the sensor usually has a high impedance at the frequencies of interest. The integration in the sensor of digital interfaces and electronic circuits to process the signal allow the amplification, the filtering, buffering and the multiplexing. The amplification of the signal in the sensor before of the transmission of the value has 2 advantages, the first one is the increasing of the signal/noise ratio thanks to the reduction of the environment noise and the second one is that it allows the use of all the dynamic range of the analog digital converter (ADC) for the sensors that implement it. To amplify the signal CMOS or bipolar transistor can be used, the CMOS transistors are probably the most suited one for the integration into the sensors, they have a high gain, high input impedance and the circuit is simple and compact. They are from 3 to 5 times smaller than the bipolar ones and that make possible to integrate tens of them in a single chip.

There are advanced transistors that allow the programming, it is possible to change the signal/noise ratio and optimize the output range of the analog digital converter.

Further than this in this signal amplifier it is possible to integrate the signal


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filtering. For system that are make of several sensor in multiplex the filtering of the signal is required to avoid problems like the aliasing that can introduce high frequency noise and cover the low frequency signals.

Another important advantage is the reduction of the number of the output wires, the multiplexing can reduce the number of wires for the sensor but also the cables required to acquire the sensor.

The reduction of the wires that are connected to the sensor package it is important for its simplification, reduce the cost and increase the reliability of the system where the sensor is used.

In addition to the primary functions these circuits are used to execute secondary functions like the self-testing of the analog circuits and the dynamic offset adjustment to allow the full use of the dynamic range of the ADC.

6.2.2 Digital control and manipulation One of the most important requirement for the smart sensor it is the compatibility with the digital control and the microprocessor based systems. The sensor has to provide an output in digital form accessible by a digital bus. Once the data from the sensors have been digitalized it is possible to process the signal to fix errors and imperfection. These data manipulation include offset elimination, auto-calibration, self testing, error identification and linearity correction.

The analog digital converter is the principal circuit required before the digital control and the manipulation of the data was possible.

After the data has been converted in digital form it is possible to execute some operation on the values, for example the auto calibration.

The sensor is calibrated in during the test in fabric but it has to have the capability to auto calibrate on the field. The compensation of the data coming from the sensor it is one of the biggest advantage of the smart sensor.

The compensation can be used to adjust the sensibility of the parameter and to correct the non linearity.

Apart from the calibration and the compensation of the signal the other important functionality required to a smart sensor are the auto-testing and diagnostic. The capability to auto test it is very interesting because allow the control system to determine the status of the sensor without the need to remove it from its place. The auto-test procedure can be activated by the control system periodically to check that the sensor is working properly.

One of the important characteristic of a sensor in the industrial sector is the reliability. Especially for distributed systems where the sensors are placed in hardly accessible places it is important that the sensor is reliable because exchanging it can be cumbersome. An easy way to increase the reliability is the


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redundancy of the sensor, the smart sensor thanks to their small size can be useful for this.

6.2.3 Communication and bus interaction A smart sensor should be able to communicate with the control system its information, so a part of the circuits of the smart sensor are assigned to the communication.

A smart sensor should be able to communicate with several buses and use different protocols, this is the goal of the IEEE 1451 standard.

The sensor and the control system have to exchange a wide amount of different information, like calibration data, parameters and data. The sensor should be able to receive and send information through the bus no only from and to the control system but also between other sensors.

6.4 Characteristics

The smart sensor is mainly composed of and internal arithmetic unit (microprocessor, microcontroller), a memory support, a conditioning signal system, one or more transducers and their relative electronics and a communication interface.

The physical transducer senses the physical quantity and converts it into an electrical signal.

The signal is fed into an A/D converter that will produce a digital value that the microprocessor is able to use.

The microprocessor performs the signal processing on the data and it handles the communication.

The communication interface is the fundamental element for a smart sensor.

The principal parts of the sensor are (Ziani et al.,2000):

• Conditioning stage: standardize the transducer signals

• Digitalization stage: multiplexing, sampling and digital conversion of the value

• Processing stage: values are converted, stored or displayed, threshold or alarms are checked.

• Communication interface: values or information are transmitted to a superior level


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If the sensor is power by battery the system power consumption is a critical element, the main requirements are low duty cycle samples and a very low quiescent power drain in order do achieve an installed life-cycle of several years.

The advantages of a local intelligence are:

• Disturb immunity and robustness, the signal is digitized or processed near to the transducer, the parasite currents and the degradation of the signal during the transmission through the wire are eliminated, this lead to a better resolution, higher accuracy and a better stability, especially in severe environmental condition.

• Distributed computing power, the processing of the signals is decentralized so the acquisition station can concentrate on other tasks, the sensor is not affected by any changes in the system with advantages in reliability and accuracy and the data can be transferred on real time.

• Auto calibration and feedback correction, these function can be executed locally automatically or after a request from the higher level.

• Lower cost, all the components of the sensor can be integrated in one microprocessor

• Lower cost to process the signal, the normalization of the value is done by the sensor and the sensor can check threshold and alarms, reducing the programming requirements on the higher level

• Easy connection, less or no cables are required to connect the sensor to the system.

• Reduced maintenance cost, the sensor is checking its own status so there is no need to periodic checks, also the exchange is easier because there is no need to configuration.

• Identification, the sensor is identified by a code or an address in order to dialogue with the higher level or with the other sensors, with the plug-and-play capability when a sensor is exchanged it is enough to give to the new sensor the same id as the previous one and it will be reconfigured automatically by the system.

• Self-diagnosis: the sensor is able to identify its faulty status and communicate up to the higher level

The constrains are:

• Environmental constraints, the electronics must be able to support the environment where the sensor is placed (temperature, pressure, moisture, etc), while with normal sensors the electronics are placed in a safer zone.


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• Power supply, when a great number of sensors must be installed the battery or solar panel can be utilized to supply a group of sensors to reduce the cabling

• Implementation difficulties, the develop of an acquisition chain with intelligent sensors is a long operation and require specific infrastructures, also the site where the sensor must be placed must be optimized.

• Lack of communication standard, at the moment there is not a communication protocol that has been selected as standard but every manufacturer is trying to impose its own standard, this make impossible the use of different sensors on the same network.

The motivations behind the connection of the sensors in a network are (Zhang et al. 2004):

• Cost saving: the servicing, the operation and the maintenance of the sensor is simplified, the wiring can be reduced.

• Remote monitoring: the sensor can communicate more than just the value measured but also information about its state, so a better asset management and preventive maintenance can be achieved.

• Modularity: adding a sensor to an existing network is mainly software issue.

• Flexibility: changes on the sensors or in reconfiguration of the sensor system can be done without any necessity to work physically on the sensor

• Accurate measurement and high data rates: the values are exchanged in a digital form, degradation of the signal is not present and a higher precision in the conversion can be achieved.

There are two ways to bring the data from the sensor to the acquisition level, in the simpler solution the acquisition level will access directly to the sensor, but in case of long distances or an high number of sensor a better solution is to collect the data from some sensor locally and then access to all the information in one time.

This is possible if a node of the network or a sensor is able to aggregate the information from the sensors that are connected to it or nearby.

The acquisition level has to access only to the node or the sensor and it can acquire all the data of the sub-network.

This has big advantages because the overhead of the communication is reduced dramatically and the bandwidth is better used.


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Many industrial systems cannot use cable connection between components or blocks of the system, they can be geographically isolated, installed only temporarily or on moving parts.

The wireless sensors have the advantage that the deployment is effortless, there is no need to connect wires so the installation in remote and hard-to-reach areas is dramatically simplified.

The sensor must be fault-tolerant for communication or node failures, it should return to a safe-state in a deterministic amount of time.

The tracking of the physical state of the machine or of the equipment is not active all the time to preserve the energy, the acquisition is either periodic or event-based, the sensor wake up, check the status and go back to sleep.

In case of any violation an alarm is raised.

When the smart sensor are uses the system designer work is made easier, all it is required is a high level coordination between sensor nodes and the control equipment.

The integration of many level of analog signals, expensive cabling, connectors and software are not necessary.

The economic advantages are clearly visible but there are also advantages for the end user that has to operate and maintain the equipment.

For example the traditional walk-around units for equipment vibration monitoring can be replaced with retrofit devices that can easily be networked into the existing facilities infrastructure.

Not only does this approach save on time to collect raw data, but permits much higher sampling cycles on the order of minutes

6.5 Sensor communication interface The smart sensor according to its definition has to provide additional information further than the measured value. The analog standard interfaces are not suited for this task so the smart sensors need a different way to communicate their information.

The sensors can be connected with or without wires, the wireless connection is the best choice for the smart sensors and brings their flexibility to the maximum level,


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but in some cases when the wireless communication is not possible a wired network can be the solution.

A brief description of the wireless technologies and of the wired protocol is presented in the following chapters.

6.5.1 Wireless technologies The wireless technologies use the electromagnetic waves for the communication, the advantage is the lack of the cables but the electromagnetic waves cannot go through the metal and the distance and the quantity of data are limited.

Different wireless technologies are available at the moment, here there are listed the most used (wikipedia, sensor magazine website).

-802.11: the LAN IEEE 802.11, also know as Wi-Fi, it is the most famous and used technology for the wireless communications. It uses the radio frequencies of 2,4 or 5GHz. It contains the specification of different standards:

802.11a: use the band of 5GHz and it can reach the speed of 54Mb/s. It uses orthogonal frequency-division multiplexing (OFDM), an efficient coding technique that splits the radio signal into several sub-signals before they reach the receiver, this bases on CSMA/CA protocol (each node listen before talk), and it greatly reduces interference.

802.11b: it is the lowest and the cheapest standard. It has gained popularity thanks to its cost but now with the reduction of the cost of the faster standards it is losing its appeal. It uses the frequency band in the 2,4GHz and it can reach the speed of 11Mb/s. It uses complementary code keying (CCK) modulation to improve speeds.

802.11g: It uses the same frequencies as the 802.11b but it can reach the speed of 54Mb/s. It uses the same OFDM coding as 802.11a.

802.11n: It is the last development, it uses the same frequencies as the 802.11g, the speed goes from 54Mb/s to 600Mb/s, it adds the multiple input multiple output (MIMO) technology to increase the speed

-Bluetooth: it is the most used technology when it is necessary to connect devices on short range and moderate speed (Wiberg and Bilstrup, 2001). It uses the frequency band between 2,402 and 2,480GHz. The range is about 10m due to the low power of the transmission. Bluetooth uses a frequency modulation scheme called Frequency Hopping Spread-Spectrum (FHSS), to avoid interference with other Radio Frequency sources, each node continues to change its frequency channel, this reduce the probability of interferences. In the industrial applications Bluetooth works well in noisy environments and with small amounts of data (Ramamurthy et al., 2005).


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-ZigBee: it is an open protocol for wireless communication, the specification of this protocol are in the IEEE 802.15.4 standard. It is been developed specifically for the wireless sensor communication (while the Wi-Fi and Bluetooth are for common uses). It uses the frequency of 868MHz in Europe, 915Mhz in USA and 2,4GHz in the rest of the world. It particularly suited for low cost devices and low power consumption. It is based on the CSMA/CA protocol and DSSS system to reduce the interferences.

The main characteristics of the most common technologies are stated in the following table (Sensor Magazine website):

Technology Max distance

Max speed

Frequencies

Power consumption

Modulation schema

Applications

HomeRF 50m 1-2Mb/s 2,4GHz

ISM band

100mW FHSS,

2FSK,

4FSK

Home networking solution

IrDa 1m 9,6Kb/s-

16Mb/s

1,8MHz 100mW Line of sight (LOS) with 30°

Data transfer between handheld instruments

IEEE 802.11a

100m 54Mb/s 5GHz 1mW OFDM

CSMA/CA

Industrial / home use

IEEE 802.11b

100m 11Mb/s 2,4GHz 1mW CCK Industrial / home use

IEEE 802.11g

100m 54Mb/s 2,4GHz 1mW OFDM Industrial / home use

IEEE 802.11n

100m 54-600Mb/s

5GHz 1mW MIMO SDM

Industrial / home use

Bluetooth 10m 1Mb/s 2,4GHz

ISM band

1mW FHSS, Gaussian frequency-shift keying

Peripheral communication, audio, handheld devices

ZigBee 75m 20Kb/s

40Kb/s

868MHz

915MHz

1mW CSMA/CA

Sensor communica


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250Kb/s 2,4GHz tion

Table 1: Wireless technologies characteristics

The most used technologies at the moment are Bluetooth and Wi-Fi, Harish Ramamurthy et al. (2005) have compared these two protocols and the results are:

Bluetooth -Distance: with increasing distance the delay become larger and jittery

-Traffic: with the increasing of the traffic there are no effect on the delay

-Packet Bursts: mild effect with performance degrading with more packets per burst

Wi-Fi -Distance: with increasing distance the performance degrades and the delay become larger and jittery

-Traffic: with increasing of the traffic the performance worsens, the effect is more pronounced at larger distances

-Packet Bursts: as the time to access the channel is constant the bigger payloads experience less per-byte delay

Bluetooth is better suited for industrial application scenarios where limited bursts of data need to be delivered in real-time in a noisy environment.

Wi-Fi is better where huge amount of data need to be transmitted in a less noisy environment.

6.5.1.1 Wireless network Topology Different topologies for the wireless networks have been developed, even if it's possible to replicate the wired network topology like the bus or the token ring this solution will not be efficient or easy to implement.

Here there is a brief description of the most common architectures (http://www.mesh-networks.org/).

6.5.1.1.1 Star network

The star topology is nowadays the standard for wireless networks.

One or more access point (AP) connect the wireless nodes to the network (in our case a wireless smart sensor).


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Even if the node can reach more than one access point it will communicate with only one at time. The nodes are completely independent one from the others and if they want to communicate between them they need to go through the AP. If one or more nodes are out of reach from the AP repeaters can be placed to extend the network coverage. The disadvantage of this topology is that it relies over the AP, if one of the AP is damaged or out of service a part of the network is dead.

Figure 9: Star network topology

6.5.1.1.2 Mesh network

A mesh network is composed of mobile nodes, repeaters and access point. The nodes are in our case the smart sensors that can also act as repeater. A signal will follow the best route from a sensor to the access point, the signal can follow parallel path in the network. Often a mesh network is overpopulated of nodes and repeaters to allow the possibility of multiple paths to the access point. The availability of different routes to reach the network is important in case one of the nodes is congested or out of service. The redundancy has a result a reduced distance in the wireless communication and so a better signal strength. The mesh network is generally more safe and reliable but has the disadvantage is that it requires sensor that must work also as repeater, this increase the complexity and the cost of the devices. Another disadvantage is that overpopulation of the network lead to increase the total cost.


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Figure 10: Mesh network topology

6.5.1.1.3 Hybrid network

The hybrid network combines the functionality of the star and mesh networks. The network is composed of nodes, repeater and access point. The nodes are simply wireless sensors, every one is connected to a repeater situated nearby. This network combines the advantages and disadvantages of the 2 networks. Thanks to the star topology the sensor are simple sensors without the repeater functionality. The mesh network brings the redundancy and the reliability. The disadvantage is that if the repeater of one of the star network is out of service this network part are unreachable. Another disadvantage is that the redundancy and the complexity of the repeater increase the cost of the network, even if less than the mesh network.


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Figure 11: Hybrid network topology

6.5.1.2 Wireless WAN technologies The technologies described before are usable for a plant size network, another way to communicate with sensor if they are out of the reach of the plant network is to use the Wide Area Network) technologies like the GSM, GPRS, UMTS and so on. These technologies can be used to send and receive data on a wide area and they rely on the mobile network of the telephone companies (GSM association www.gsmworld.com).

-GSM: (Global System for Mobile communications) it was born in the 1991 for mobile phones, it combines vocal and data communication services

-GPRS: (General Packet Radio Service) it was born in the 2000, it extends the capabilities of the GSM with the ability to handle packets of data.

-EDGE: (Enhanced Data rate for GSM Evolution) it was born in the 2003, it is and enhancement of the GPRS

-UMTS: (Universal Mobile Telecommunication System) it was born in the 2006 and it is the actual evolution of the GSM.


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6.5.2 Wired technologies Even if the best benefits of the smart sensors are achieved with the wireless communication of the sensors in some cases the wired communication is preferred or it is the only solution available.

Here there is a brief description of the most used wired technologies:

-EIA 485: most known as RS-485 (www.eia.org) , it is a standard published by the ANSI Telecommunication Industry Association / Electronic Industry Alliance (TIA/EIA), it specify only the electrical characteristic of the driver and the receiver, it does not specify or recommend any communication protocol. The physical media is a twisted pair of wires and the maximum distance is 1200 meters. Multiple Drivers and Receiver are possible. Point-to-point, Multi-dropped and Multi-Point topologies are utilizable.

-EIA 422: also known as RS-422 (www.eia.org), it is a standard published by the ANSI TIA/EIA, like the EIA 485 it specifies only the electrical characteristic of the driver and the receiver and no specification about the protocol is inside the standard. The physical media are 2 twisted pair of wires, the maximum distance is 1200m. In contrast to the EIA 485 the EIA 422 does not allow multiple Drivers so the Multi-Point network topology is not usable, Point-to-point and Multi-dropped topologies are usable.

-RS-232-C: it is a standard published by the ANSI EIA (www.eia.org), it specify the electrical and mechanical characteristic but does not specify the protocol. The physical media are 3 wires (2 for data and 1 for ground) and the maximum distance is 300m. Only the point-to-point communication is allowed.

-Ethernet: ISO/IEEE 802/3 it is a standard published by the IEEE (www.ieee.org), it defines the number of wire and electrical signals for the physical layer as well as a common addressing format. It uses 4 or 8 twisted pair of wires and the maximum distance is 100m. Point-to-point, Multi-dropped and Multi-Point topologies are allowed.

-Optical fiber: The physical media is a transparent fiber made of very pure glass (silica) that acts as a waveguide or “light pipe” to transmit the light between the two ends of the fiber. It is used when longer distances or higher bandwidth are required, it has the advantage that it is immune to the electromagnetic interference. Point-to-point, Multi-dropped and Multi-Point topologies are allowed.

Some technologies like Profibus are derived from these.


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6.5.3 Communication protocols More than 60 different sensor network protocols are available at the moment, every one has different functionality and characteristics.

Here there is a list of the most popular sensor buses or networks:

-ASI: “Actuator Sensor Interface”, was developed in Germany by a consortium of sensor suppliers. A low cost, bit-level system, designed to handle 4 bits per message for binary devices in a master-slave structure operating in distance up to 100 meters. It is designed mainly for factory automation and process control environment (see www.as-interface.net for more information).

-HART: “Highway Addressable Remote Transducer”, is a network promoted by Rosemount Inc., which provides two-way digital communication atop traditional 4-20mA loops at the rate of 1200 bps. Utilization of continuous analog signal as the primary process signal makes it well suited for continuous and hatch control applications. And for many users with legacy control systems and have difficulty in justifying holistic retrofits or migrations to newer all-digital technologies, HART is a simple yet effective solution. It is still popular in sensor network market (see www.hartcomm.org for more information).

-FF: “Foundation Fieldbus”, was formed from the merging of components of specifications by WorldFlP and Profibus supporters to test and demonstrate fieldbus components to support an eventual single, universal fieldbus standard. However, only 250,000 FF-enabled instruments are currently in use, despite the fact that FF technology has been available for around five years. The technology of FF has yet to demonstrate enough value for wider user acceptance (see www.fieldbus.org for more information).

-Profbus: “Process Field Bus”, was developed in Germany and strongly supported by Siemens. It is German DIN Standard 19245. It consists of 4 parts. Part 1&2 are designed as Profibus-FMS and cover automation applications in general. Part 3, Profibus-DP, is a faster system for factory automation applications. Part 4, Profibus-PA, is in preparation for process control applications (see www.profibus.com for more information).

-CAN Bus: “Control Area Network”, was developed in Germany by Robert Bosch GmhH with Intel and Philips in the early ‘80s for automotive in-vehicle networking, Selectable baud rated up 1 Mbps, and twisted pair, fiber, coax, and RF media is supported. CAN is IS0 Standard 1 1898, approved for passenger vehicle applications. CAN-based systems were approved by SAE as Standard J 1850 for American passenger cars and Standard 11939 for trucks and large vehicles (see can-cia.org for more information).

-DeviceNet: An application protocol built on top of CAN, developed by Allen-Bradley. It features the use of object-oriented software and is used primarily in


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industrial control systems. It uses a 4-wire (signal pair and power pair) shielded cable and can support up to 64 nodes per network segment at speeds up 500Kbps at l00m or 125Kbps at 500m. An Open DeviceNet Vendors Association (ODVA) has been formed (see www.odva.org for more information).

-Indrustrial Ethernet: Industrial Ethernet has become a byword for forward thinking industrial networking in the 21st century. Many industrial fieldbus vendors are encapsulating existing protocols in TCP/IP. Presently there are four major contenders: ModbusTCP (Modbus protocol on TCP/IP), EtherNet-IP(the ControlNet and DeviceNet objects on TCP/IP), Foundation Fieldbus High-speed Ethernet, and ProfiNet (Profibus on Ethernet).

The main characteristics of the most common protocols are stated in the following table (adapted from Zhang et al. 2004):

Fieldbus Master Max segment length

Max speed

Wires

Max stations

Standard

ASI Single 100m 167kb/s 2 32 EN50295

BITBUS Multi 300m@375kb/s

500m@125kb/s

375kb/s 2 251 IEE1118

ISO11898

CAN Multi 40m@1Mb/s

5km max

1Mb/s 2 64 ISO11519

open

ControlNet Multi 250m/48nodes

500m@125kb/s

5Mb/s Coax 99 Specified

open

DeviceNet Multi 100m@500kb/s

2km max

500kb/s 4 64 Specified

open

Foundation Fieldbus

Multi 9,5km max 31,25kb/s 2 240 Specified

FIP Multi 2km@1Mb/s 2,5Mb/s 2 256 EN50170

INTERBUS Single 12,8km max 500kb/s 8 255 EN50253

LON Multi 6,1km@5kb/s 1,2Mb/s 2 2 ANSI

Modbus plus

Multi 1,8km max 1Mb/s 2 32 Proprietary


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Profibus FMS

Multi 19,2km@9,6kb/s

200m@500kb/s

500kb/s 2 127 EN50170

Profibus DP Multi 1km@12Mb/s 12Mb/s 2 127 EN50170

Profibus PA Single 1,9km 93,75kb/s 2 32 EN50170

Seriplex Single 300m 250kb/s 4 510 Proprietary

HART Single Depend on physical media

Depend on physical media

15 Open

Table 2: Fieldbuses characteristics

6.5.4 Initiatives Several initiatives are actives at the moment both from the industrial side and the academic one for the development of smart sensors.

6.5.4.1 Industrial initiatives Several open standards for industrial protocols have been developed especially for the wired communication like CAN, DeviceNet and ControlNet.

The OPC foundation is trying to establish a standard for the exchange of the data between systems from different manufacturers.

Some producers have developed sensors that meet the requirements of the 1451 standard like

6.5.4.2 Academic initiatives Early work regarding the wireless sensor networks was started by the DARPA for the military surveillance and distributed network project, with the low-power wireless integrated microsensor (LWIM) and the SenseIT projects.

The UCLA in association with the Rockwell Science Center have developed the Wireless Integrated Network Sensors (WINS) and the NIMS, these projects deals with the ad-hoc wireless sensor networks and the development of MicroElectronics Mechanical Systems (MEMS). These projects are military based and they use nonstandard RF communication technologies.

UC Berkeley has developed the Motes and the Smart Dust projects, the focus was


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to create low cost-microsensors. They developed also the TinyOS operating system, an embeeded operating system particularly suited for small microcontrollers that are used in the sensors.

The Pico-Radio project group at UC, Berkeley has developed a low power data acquisition project to acquire sensors data using mesh networks.

6.6 RFID Along with the smart sensors also the RadioFrequency Identification (RFID) can be used to acquire information.

Most of the RFID tags are passive tags.

A Passive tag is a read-only device, it contain no on-board power supply, usually it is equipped with limited memory and only carry EPC code, detailed product information resides in back-end information systems.

When a passive tag moves into a reader’s working range, it is powered, and then can communicate with reader to send out EPC code.

An Active tag is more powerful, it contains on-board power supply, can be writeable; it may also integrate with sensors, such as temperature, humidity and vibration sensors, etc., to monitor the environment parameters an object experienced when passing through the supply chain.

EPC code is the reference point to retrieve all related information, and it is because of this universal unique code scheme makes Auto-ID Center developed RFID system differs from traditional ones, so this type of RFID system is also called “EPC network”.

6.7 Standards

6.7.1 IEEE 1451 The IEEE 1451 describes a set of open, common, network independent communication interfaces for connecting transducers (sensors or actuators) to microprocessors, instrumentation systems, and control/field networks.

The 1451 defines a common set of interfaces to access the transducer data whether the transducers are connected to systems or networks through wires or wireless.

The goals of the 1451 are:

• Develop network-independent and vendor-independent transducer interfaces

• Support a general model for transducer data, control, timing,


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configuration and calibration

• Specifies physical and functional interfaces between sensors/actuators and instruments/microprocessors/ networks

• Specifies analog, digital and wireless interfaces make easier the connection of sensors and actuators either by wires or wireless methods

• Provide self-describing capabilities of the sensor characteristics via the TEDS

• Allows sensors to be installed, upgraded, replaced and/or moved with minimum effort

• Eliminates the errors due to the manual entering of data and system configuration steps.

The schematic of wireless sensor data and TEDS acquisition as per IEEE 1451 is illustrated in figure 12.

Figure 12: Wireless sensor data schematic

The standard is divided of various parts to describe the components of the sensors and their use.


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IEEE 1451.1

IEEE 1451.1 defines a common object model describing the behavior of smart transducers. It defines a measurement model that streamlines measurement processes. It also defined the communication models used for the standard, which included the client-server and publisher-subscriber models.

IEEE 1451.2

IEEE 1451.2 defined a transducers-to-NCAP interface and TEDS for a point-to-point configuration. Transducers are part of a TIM. The original standard describes a communication layer based on enhance SPI (Serial Peripheral Interface) with additional hardware (HW) lines for flow control and timing. This standard is being revised to add support for the popular serial UART interface.

IEEE 1451.3

IEEE 1451.3 defined a transducer-to-NCAP interface and TEDS for multi-drop transducers using a distributed communications architecture. It allowed many transducers to be arrayed as nodes, on a multi-drop transducer network, sharing a common pair of wires.

IEEE 1451.4

IEEE 1451.4 is a new standard for adding plug and play capabilities to analog transducers. The underlying mechanism for plug and play identification is the standardization of a TEDS. IEEE 1451.4 defines the method of encoding TEDS information for a broad range of sensor types and applications. In order to cover such a broad range while also keeping memory usage to a minimum, the IEEE 1451.4 TEDS concept utilizes the concept of templates that define the specific properties for different sensor types. IEEE 1451.4 defined a mixed-mode interface for analog transducers with analog and digital operating modes. A TEDS was added to a traditional two-wire, constant current excited sensor containing a FET amplifier. The TEDS model was also refined to allow a bare minimum of pertinent data to be stored in a physically small memory device, as required by tiny sensors. Templates are used to describe the data structure of TEDS. The current templates cover accelerometers, strain gauges, current loop sensors, microphones, thermocouples and more. The IEEE 1451.4 specification defines a TEDS as consisting of multiple sections chained together to form a complete TEDS. The first section is the basic TEDS, comprising of the essential identification information. These are enlisted in the following table.


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Item Bit length Allowable range

Manufacturer ID 14 17-16381

Model number 15 0-32767

Version letter 5 A-Z

Version number 6 0-63

Serial number 24 0-16777215

Table 3: TEDS information

IEEE 1451.5

IEEE P1451.5, which is in development phase, defines a transducer-to-NCAP interface and TEDS for wireless transducers. Wireless communication protocol standards such as 802.11 (Wi-Fi), 802.15.1 (Bluetooth), 802.15.4 (ZigBee) are being considered as some of the physical interfaces for IEEE P1451.5. One should be able to get the same sensor data from the wireless sensor implementing any of these three wireless protocols.

IEEE 1451.6

IEEE P1451.6 which is in development phase, defines a transducer-to-NCAP interface and TEDS using the high-speed CANopen network interface. Both intrinsically safe and non-intrinsically safe applications are being supported. It defines a mapping of the 1451 TEDS to the CANopen dictionary entries as well as communication messages, process data, configuration parameter, and diagnosis information. It adopts the CANopen device profile for measuring devices and closed-loop controllers

Transducer Electronic Data Sheet (TEDS)

The idea behind wireless sensor/transducer data acquisition would be to have data from sensor in a wireless manner complying to the IEEE 802.11 standard for wireless LAN (local area network). The transmitted data could be TEDS (transducer electronic data sheet) information as per ongoing IEEE 1451.4 standard).

A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor.


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TEDS is intended towards sensor self-identification and description. Primarily, TEDS information resides on a memory chip (EEPROM) within the sensor and is accessed by measurement systems via serial interface. TEDS comprises of information like manufacturer, model number, serial number, measurement range, sensitivity, calibration parameter, etc.

Alternatively, a virtual TEDS can exist as a separate file, downloadable from the internet.

Software architecture

The software architecture consists of four modules: smart transducer interface module (STIM), transducer electronic data sheet (TEDS), transducer independent interface (TII). Figure 13 shows the basic layout of the software modules

Figure 13: Smart sensor software architecture


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6.8 Smart bearings The smart bearings are special smart sensors. According to statistics, a lot of the rotating machinery faults are caused by the bearings, so the smart bearing technology is important to reduce these faults.

The bearing has been widely used in the industry and commercial fields to provide support for the rotating machinery. Bearing faults can result in costly downtime, so it is important to collect and analyze the fault signals. To date, the topic of fault diagnosis for the bearing has been investigated by researchers, and some new analysis techniques can be used for the early fault detection.

Prior efforts to monitor the condition of a rolling element bearing had mounted an accelerometer or acoustic emission sensor to the machine or fixture containing the bearing. Difficulties can arise however when the machine or fixture is subject to inputs which may contaminate sensor signals. The presence of this noise can be detrimental if its magnitude is large enough to hide a bearing fault signature. Thus, fault detection efforts would benefit from moving the sensors closer to the source of a bearing fault and away from surrounding noise sources.

R. X. Gao et al. (2003) with the support of FAG and SKF have developed a smart bearing attaching sensors to the bearing faces, this solution does not affect the integrity of the bearing but it increase the overall dimension.

B. T. Holm-Hansen and R. X. Gao (1997) proposed the inclusion of the sensors and the microprocessor in the external raceway, the integration of the sensor in the bearing can be achieved without exceeding the standard dimensions, this has the advantage that the smart bearing can be placed in the existing machines without the need to modify its support. The sensors are located very close to the source of a possible bearing fault to reduce the environmental noise. The prototype version of the smart bearing includes only force sensing elements but its evolution can include temperature and acoustic emission sensors. Another future improvement will be the possibility to transmit the data through a wireless connection to an external receiver via RF telemetry. This solution has the disadvantage that the physical characteristics of the bearing are modified and weakened.

Yimin SHAO et al. (2008) presented another solution with multiple sensors embedded in the bearing, two accelerometers, two speed sensing devices and one temperature probe are embedded in a protuberant part of the inner raceway and another temperature probe is installed on the outer raceway. The border of the outer raceway is bended to the center, this bended part is embedded in the groove manufactured on the outer raceway of the bearing. Two vertical vibration acceleration signals, the temperature signals of the outer and inner raceway and the speed signals are available.


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Figure 14: Structure of the smart bearing

6.9 Consideration about the smart sensors

Looking through the website and the catalogues of the companies that produce components for automation (e.g. Siemens, Omron, Baumer) it is possible to find some smart sensors that are designed for very special tasks but there is no trace of sensors compatible with the 1451 standard.

The smart sensors on the paper have a lot of advantages and interesting features but they are not so diffused even if the specification and the ideas are available since the '80.

One of the reasons behind the poor success of the smart sensors is that the industry giants have not decided to switch to this new technology.

When a big company start to use a new technology they will ask their supplier to use them in the new equipments they buy.

The suppliers start to use the new technology in their machine and gradually they put it in as a standard in the new equipments they make.

Now even the smaller companies when they buy new equipments they will have it equipped with this technology.

Also the big sensor producers (that are also the producer of most of the biggest


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plant) have not started to push this technology.

All the companies have a warehouse of spare parts, they need to have it to reduce the down time of a machine when a sensor is broken, so one of the reason why the change to the new sensors is slow it is also economic, they will need to have in their warehouse spare sensors and now that there is not a accepted standard they will need to have a lot of different sensors.

Another reason it is that all the functionality of the smart sensors can be done at the moment by the control system, the check of alarms, threshold or so.

The industrial sector does not welcome any innovation and accept it immediately.


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ATION

YEAR

WORK

CONTRIBUTION

Reconfigurable smart sensors for dependable systems

Erfaan Sharif, Andrew Richardson &Tony Dorey


Implementation of smart sensor using a non linear observer and fuzzy logic

H. Benitez-Perez, H. A. Thompson, P.J. Fleming

UKACC International Conference on CONTROL ‘98


An Open-Standard Smart Sensor Architecture andSystem for Industrial Automation

Wayne G. Baer, Richard W. Lally

Specific knowledge

The smart sensor design in industrial process application

M. Ziani M. Bennouna M.Amamou M. Barboucha,A.Melhaoui

10th Mediterranean Electtotechnical Conference, EleCon 2000, Vol. I 115


A Low-cost Internet-enabled Smart Sensor

P. Ferrari, A. Flammini, D. Marioli and A. Taroni

Specific konwledge

Smart sensor modelling with the UML for real-time embedded applications

Christophe Jouvray, SCbastien GCrard, Franqois Terrier,Samir Bouaziz, Roger Reynaud

IEEE Intelligent Vehicles Symposium


Progress of Smart sensor and Smart sensor networks

Yong Zhang and Yikang Gu,Vlatko Vlatkovic,Xiaojuan Wang

Proceedings of the 5" World Congress on Intelligent Control and Automation,



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June 15-19, 2004. Hangzhou. P.R. China

An architecture for Intelligent Systems Based on Smart Sensors

John Schmalzel', Fernando Figueroa', Jon Morris3, Shreekdnth Mandayam', Robi Polikar

IMTC 2004 ~ lnalumnlmion and Measurement Technology Conferace Coma Italy, IS-20


Wireless Smart Sensors Networks Overview

A. R. Al-Ali, Y. R. Aji, H. F. Othman and F. T. Fakhreddin

Specific knowledge

Smart Sensor Design: The Art of Compensation and Cancellation

Kofi A.A. Makinwa, Michiel A.P. Pertijs, Jeroen C. v.d. Meer, Johan H. Huijsing

IEEE


Towards Networked Smart Digital Sensors: a Review

Abhisek Ukil

IEEE


Networked autonomous smart sensors and dynamic reconfigurable application development tool for online monitoring systems

Zhao Jiangbin, Wu Jiankang, Shi Tielin,Xuan Jianping

IEEE


Demonstration of the Plug-n-Play of Smart Sensor Nodes

Ashwin Juvva, and D. Gurkan, Suman Gumudavelli, and Ray Wang

IEEE


9-Ary Tree Based Self-Immunity Method for Smart Sensors

Guojian Huang, +Guixiong Liu, Xiaobin Hong, Tiequn Chen

IEEE



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Programming abstraction and middleware for building controlsystems as networks of smart sensors and actuators

Sebastian Zug, Michael Schulze, André Dietrich, Jörg Kaiser

IEEE


Next Smart Sensors Generation

Stéphane Gervais-Ducouret

IEEE


Integrated Microsensor Module for a Smart Bearing with On-line Fault Detection Capabilities

B. T. Holm-Hansen and R. X. Gao

IEEE Instrumentation and Measurement Technology Conference Ottawa, Canada


An Integrated Design Approach for Self-validating fault tollerant smart sensors

E. Sharif, A.M.D. Richardson and A.P. Dorey

IEEE


The wireless sensor networks for factory automation: issues and challenges

L. Q. Zhuang, K. M. Goh and J. B. Zhang

IEEE


Fault diagnosis System based on smart bearing

Yimin SHAO, Liang GE and Jieping FANG

International Conference on Control, Automation and Systems


Smart bearing technique based on the micro-sensor

R.X. Gao and Q. Lv

China mechanical engineer, 14(21)


Smart sensor platform for industrial monitoring and control

Ramamurthy H., Prabhu B.S., Gadh R., Madni A.M

Proceedings of the 4th IEEE Conference on Sensors, 1116-1119.



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Cap. 7 Handheld devices

This chapter presents the use of handheld devices as a support for the maintenance tasks.

7.1 Description

The way in which the maintenance personnel are working is not changed very much in the last decades, and still nowadays almost all the information are shared in verbal way between maintenance personnel (MIMOSA).

When a problem occurs the operator call the maintenance department, then one or more people are sent to fix the problem.

They have to walk to the machine, have a first look, identify the problem and its cause, go back take the tools and spare parts and then carry out the work.

Industry experts have pointed out for many years the low productivity levels in maintenance departments of most companies around the world. They cite anywhere from 30% to 50% as an average for “wrench time” (Palmer, 2007 www.maintenanceworld.com), that is the productive time technicians spend actually repairing or replacing equipment, as opposed to walking to the job, receiving instructions, waiting for parts and other productive or non-productive activities.

If the system can give them all the information like which part has failed, which tools they need and where the spare part is located, the repairing time could be dramatically reduced.

The main question is from where the data about the necessary operation can be acquired?

The most obvious source of information is the CMMS, as most have a wealth of reporting tools available.

If all the information like the procedure to exchange a part or to repair it, the tools needed, spare parts required are inserted in the system, the maintenance personnel can access in a few time and even if they have not carried out that task before they could have at least a guideline to solve the problem.

This has also the advantage to standardize the procedure for the repairs, otherwise every person keeps his own way of work and his own way to fix the problem that not always are the best ones.

There is also another advantage, a CMMS can track actual hours charged to a work order versus planned hours, or report on actual hours versus budgeted hours


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for a given cost center. Through mobile technology, the CMMS can monitor progress of individuals working on various jobs, or even their physical location using GPS devices (Kans et al., 2009).

But the picture is not yet complete with these reporting tools. For example, if eight hours are charged to a given work order, how many hours were spent actually working on the machine? This level of detail is hard to capture using a CMMS, and it is not generally easily verifiable. Work measurement techniques, such as predetermined or video-based time and motion study, can get at the specifics. However, these tools can be quite unpractical for maintenance workers that move very much around the facility carry on many non-repetitive tasks. Work sampling is one tool that is effective in these situations.

At the moment all the manual regarding the machine are provided by the supplier both on paper and on digital form, one of the paper copies of the documentation is normally left near the machine so the operator or the maintenance personnel can have a look at it in case they need to check anything.

To standardize the way in which the documentation is made some standards are available like the ISO/TC 10 [http://www.iso.org/iso/iso _technical_committee.html?commid=45986] that focuses on the standardization and coordination of technical product documentation (TPD). TPD includes technical drawings, manually produced or computer based for technical purposes throughout the product life cycle, in order to facilitate document management and use, such as preparation, storage, retrieval, exchange, and reproduction.

The use of PDA or tablet can be seen as an aid during the maintenance tasks. They can be used to retrieve information from the company network, access to the knowledge base, read the value of some sensor during the periodic check and have an immediate feedback in case something is wrong, saving the time to go back to the office, download the data and check it offline.

The time, that is saved thanks to this, can be used by maintenance workers to upgrade their skills, or train others such as junior staff and even operators.

7.2 Handheld devices use cases It is possible to use the mobile devices for the so-called “walk-around inspections”: devices can be used during a visual inspections done by the maintenance workers for example to check the previous state of the component and compare it.

The requests for intervention or the work orders can be sent to the central server


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and from there the maintenance personnel can retrieve the list of the job that have to be carried out with the information about time required for the repair and the importance order and then select which task has to be carried out first.

They can check the availability of spare parts, the procedures for the maintenance tasks and check the actual status of the equipments.

The above solution can be improved if the sensors or tools can be connected to the handheld devices, in this case the maintenance personnel can carry out some measurements (e.g. vibration monitoring). Traditional tool for vibration analysis and use of PDA to this end was analysed in literature, see Fumagalli et al. (2009).


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7.3 Augmented reality In the research of S. Henderson et al. (2011) the handheld support for the maintenance operation has been further developed with the use of the augmented reality to provide useful information to the mechanics performing maintenance and repair task in field setting. Text, labels, arrows and animated sequences are shown on the headworn display to facilitate the comprehension, localization and execution of the tasks.

The majority of the maintenance and repair operations are carried out by trained personnel applying established or standardized procedures, these procedures are typically a sequence of tasks that have to be carried out on a particular item in a specific location.

There are several systems or technologies that can support the mechanic in his job, such assistance is desirable, even for the most experienced workers, for several reasons. First, navigating and performing maintenance procedures imposes significant physical requirements, for each task of a procedure the mechanic has to move his body to locate and orient to the component that is the target of the task and then he has to perform other physical movements to carry out the task. The optimization of these movements can save time and energy, especially if the tasks that have to be carried out are unfamiliar to the worker. Second, the localization of the part and the movement impose cognitive requirements, the worker must first spatially frame each task in a presumed model of the larger environment and maps his location on the physical world. Third, the worker has to interpret and comprehend the task. Assistance in this step can reduce the mental workload required from the worker.

The task that have to be carried out are classified in two classes according to the definition given by U. Neumann et al. (1998), the first one are focused on the cognitive, or informational, aspects of the maintenance task, the second are focused on the workpiece aspects of the task. The informational phase activities include for example the localization, instruction comprehension and the transposition of the information of the instructions to the physical environment; the workpiece phase activities include comparing, aligning, adjusting and all the other physical manipulation operations. The augmented reality can support the worker in both of the type of tasks.

The augmented reality can show on the display that the worker is wearing arrow to indicate the position of the component, if the target component is behind the worker an arrow will show him the shortest rotation direction to the target, once the target is in visual field an arrow will direct his attention to the target and when he has localized the part the indication will disappear and the action information will be shown on the screen like the tools needed and the correct procedure.

Some of the main advantages of the augmented reality approach are:


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• Reduction of the time to locate a part or task, statistically the worker can locate faster the part compared to the standard method and even compared to the use of an handheld device.

• Reduction of the unnecessary movement during the repair


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ATION

YEAR

WORK

CONTRIBUTION

A mobile maintenance decision support system

Campos Jaime, Om Prakash, Errki Jantunen

Maintenance and asset management vol 23 no 2


Ontology of mobile maintenance processes

Fumagalli, L., Jantunen, E., Macchi, M.

the 2009 conference of the Society for Machinery Failure Prevention Technology, Dayton, OH, USA, April 28-30, 2009.


Smart sensor platform for industrial monitoring and control

Ramamurthy H., Prabhu B.S., Gadh R., Madni A.M.

Proceedings of the 4th IEEE Conference on Sensors, 1116-1119


The advancement of maintenance information technology

Mirka Kans



Exploring the Benefits of Augmented Reality Documentation for Maintenance and Repair

Steven Henderson and Steven Feiner

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 17


Cognitive, Performance, and Systems Issues for Augmented Reality Applications in Manufacturing and Maintenance

U. Neumann and A. Majoros

Proc. IEEE Virtual Reality (VR ’98), pp. 4-11



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Impact of Information on the Quality of a Prognosis Indicator

Ariane Lorton, Mitra Fouladirad and Antoine Grall

IEEE


Cost of poor maintenance - A concept for maintenance performance improvement

Antti Salonen and Mats Deleryd

Journal of Quality in Maintenance Engineering Vol. 17 No. 1


Integrating industrial maintenance strategy into ERP

K. Nikolopoulos, K. Metaxiotis, N. Lekatis and V. Assimakopoulos

Industrial management & Data Systems 103/3 pag. 184-191


The implementation and deployment of an ERP system: An industrial case study

Claire Berchet and Georges Habchi

Computers in Industry 56 pp. 588–605


ERP use: exclusive or complemented?

Liane Elbertsen, Jos Benders and Ed Nijssen

ndustrial Management & Data Systems Vol. 106 No. 6, pp. 811-824


Supporting total productive maintenance by mobile devices

Thun J.H.

Production Planning and Control, 19:4, 430-434



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Cap. 8 Analysis of an industrial case

In this chapter an industrial case is presented. The case shows common maintenance operations related to some production machines. Analysis of possible improvement through the adoption of new technologies (i.e. the one described in the previous chapters) is carried out. Hypotheses are made for the implementation of the innovation described in this work. At the end comments about why new technologies are not yet implemented is provided.

The machines that are analysed are manufactured by BARTEC Dispensing Technology. The profile of the company is described in the following paragraph.

8.1 Manufacturer company profile BARTEC Dispensing Technology is based in Weikersheim (Baden-Württemberg). It is one of the world‘s leading providers of dosing and metering technology systems. With more than 2,300 machines installed worldwide BARTEC Dispensing Technology can be counted among the leading manufacturers of dispensing and impregnation technology for the processing of reactive resins and system providers for assembly and production automation.

BARTEC Dispensing Technology delivers its highly complex equipment to all important manufacturers in the automotive, industrial electronics, medical, filter and solar industry.

It is part of MAX Automation AG which is an entrepreneurially active international investment group, which occupies leading positions with medium-sized industrial companies and target oriented markets and has focused on the automation of manufacturing processes. In total it comprises over 1,000 employees and lots of machine building and automation know-how.

The company develops technologically complex solutions to process liquid and paste-like reaction molding resins, as well as systems for the automation of assembly and production processes, particularly for electronic components. Besides its technological expertise, BARTEC Dispensing Technology has extensive engineering know-how in the area of resins and their process characteristics. Following the integration of FAS Automation GmbH, BARTEC Dispensing Technology has developed into a full-range supplier of automation solutions for the manufacture of electronic components.


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Figure 15: Example of a dispensing system

BARTEC Dispensing Technology‘s product portfolio is being successively expanded.

In 2008, the company entered the market for impregnating plants for electro-motors, stators and rotors.

Figure 16: Rotor impregnation


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In 2010, plasma pretreatment, which improves the adhesive properties of material surfaces, was introduced.

Figure 17: Plasma system

BARTEC Dispensing Technology also offers heat staking as an alternative joining technique, which enables thermoplastic synthetics to be reformed using localized heat.


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Figure 18: Heatstake system

Four of the company‘s own sales companies based in Belgium, the UK, Italy and the US, as well as a business unit in China, are primarily responsible for the global marketing of all products. BARTEC Dispensing Technology‘s customers range from the automotive industry through to electronics and electrical goods producers, filter manufacturers and medical technology companies.


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8.2 Machine 1

The first example it is a machine made for the potting of sensors for an automotive supplier.

8.2.1 Machine description

The machine is composed of a dispensing cell, a conveyor, a checking station, a preheating station, a vacuum station and an automatic load/unload zone. The machine is thus a quite complex equipment that carries out different functions.

The dispensing cell is composed of a dynamic mixing head where the two component of the material are mixed to be dispensed over the part, this mixing head is mounted on a 3 axis system so it can reach every position in the working area, the material is heated up and degassed in the MPS (material preparation system) machine.

The machine is controlled by a softPLC running on an industrial pc, on this pc serial, USB and Ethernet ports are available for the communication with other devices.

The process is the following: the parts are placed on a pallet by a robot, when the loading process is completed the robot cell writes on the RFID tag mounted on the pallet the information about which place is occupied and which type of part is there, after this the pallet is released.

The pallet goes to the checking station where the presence of the part and its correct position and alignment is checked through several laser sensors, the information from the sensors are compared with the RFID ones.

After this check the pallet moves to the preheating station where the parts are heated for a better dispensing result (the resin is heated to reduce its viscosity, if the parts are cold when the resin goes over them it is cooled down and its viscosity increases and it will not be able to go into all the small gaps).

When the preheating is finished the pallet reaches the pre-dispensing place, the temperature of the parts is checked through an optical pyrometer to ensure that the temperature is in the correct range and then the first part of the dispensing process is done, the mixing head is moved over each part present on the pallet and the resin is dispensed in a small hole present on the part.

After the pre-dispensing phase is finished the pallet moves to the vacuum station where the part are placed under vacuum to extract all the air bubbles that can be present.


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The pallet moves then to the post-dispensing place where the part is filled to the limit with resin to obtain a plain surface.

The pallet is finally sent to the unload zone where the robot removes the parts from the pallet and according to the information contained into the RFID tag place them in the scrap part container or if they are good they will continue to the next machine for further processes.

BARTEC machines can be customized according to the specific needs of the customer. The machine here described is installed in a production plant (automotive sector) in Germany.

Figure 19: View of the machine 1


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The main parts of the machine are

1. Dispensing cell

2. Conveyor

3. Check station

4. Preheating station

5.Vacuum station

6. MPS: Material Preparation System

Figure 20: Footprint of the machine 1

8.2.2 Prescribed maintenance

The prescribed maintenance is explained in the operator manual, these information are combined with the experience on the previous machines and


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finally result in the maintenance plan, that is thus a combination of technical specification from the machine manufacturer, plus maintenance operators experience.

This is an a extract of the maintenance part of the manual

Daily maintenance

• Before start of work, inspect the dispensing system, in particular for loose connections, leakage, chafe marks, cuttings, bubble formation, pinches, kinks or knees.

Immediately inform your principal of any damage you have determined, secure a large zone around the working area and stop the production process until the damage is repaired.

• Check the drive shafts of the feed pumps for jerk-free and rapid movement

• Check the valve needles for jerk-free and rapid movement

• Check the filling levels of the waste bin and the blind shot vessel

• Keep the work table and the control elements in proper state

Cleaning

In case of breakdown

Element Action

Mixing head assembly (dispensing valve or dispensing pump when you use single-component systems)

- Stop the dispensing system in a proper way

- Unscrew the dispensing valves for resin and hardener from the mixing head

- Remove carefully all material residue in the valve channels of the mixing head and the valve nozzle

- Start the dispensing system

Daily

Element Action

Work table, cladding and control elements

- Remove the sealing compound and the cleaning agent with an appropriate cleaner

Waste bin and blind shot vessel

- Check the filling level

- Properly remove the waste


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Weekly

Element Action

Pneumatic maintenance unit cleaning according to manufacturer’s instructions

Silica gel filter If only the last quarter of the granule in the vessel has got orange colour, you have to replace the granules:

- Stop the dispensing system

- Remove the protective sheet

- Unscrew the cap nut and carefully remove the glass cover

- Remove the compression spring and the filter plate

- Replace the granules

The used granules ca be dried and used once again:

Dry the granules at 100 to 200 °C until they have a uniform orange colour

- Remount the silica gel filter in reverse order

- Start the dispensing system in a proper way

Dispensing unit (Mixing chamber, agitator, outlet nozzle, valve nozzle)


- Disassemble and clean the dispensing unit

-Start the dispensing system

Monthly

Element Action

Material filter (only if you use an external gear wheel pump)


- Close shut-off valve at the outlet of the material pressure vessel

- Disconnect and clean the screen of the material filter

- Open the shut-off valve at the outlet of the material pressure vessel for a moment and close it afterwards


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- Reinstall the screen of the material filter


- Close the shut-off valve at the outlet of the material pressure vessel

- Disconnect, at the dispensing pump, the material lines from the vessels and hold them over an appropriate catch tray

- Open the shut-off valve at the outlet of the material pressure vessel in order that material will be delivered until an air bubble is leaving the material line

- Close the shut-off valve at the outlet of the material pressure vessel

- After cleaning the threads, reconnect the material line to the dispensing pump

Every six months

Element Action

Material pressure vessels - Cleaning according to manufacturer’s instructions

- Properly remove waste

Lubrication

Weekly

Element Action

Hard metal sealing element - Stop the dispensing system by pressing the push button System OFF at the control

- Let two or three drops of the special oil drip through the hole Y of the mixing head on the sealing element

Start the dispensing system by pressing the push button System ON at the control

Monthly


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Element Action

Valves -Stop the dispensing system in a proper way

- Separate the pneumatic lines from the swivel connection by unfastening the cap nut

- Let two or three drops of the pneumatic oil of the pressure maintenance unit drip on the holes of the swivel connections


Worm-type pump - grease the seal space between the shaft seals

Pressure adjusting screw of the pressure reducer

- grease the thread of the screw

Every six months

Element Action

Guides for linear guiding Axes

- lubricate those parts recommended by the manufacturer

- grease axes according to attached supplier maintenance

Table 4: Prescribed maintenance for machine 1

These are the information contained in the manual, further than this there are customer specific maintenance operations.

The quality of the mixing of the materials is checked by the operator every week, the two materials are dispensed separately and their weight is checked to ensure that the mixing ratio is inside the desired tolerance.

The check quantity of the material is done automatically, the machine is equipped with a weighing device, after a defined number of parts the dispensing head is moved to over the cup on the weighing device and a check of the weight is carried out, if the weight is out of tolerance the machine is stopped and the operator has to check it.

The quality of the parts is checked through random samples, randomly the operator takes some parts from the line and the quality is checked, if the quality is below the standards the line is stopped and the machine is checked.

The number of scrap parts is monitored if the number of scrap parts in a defined period of time exceeds the warning threshold the line is stopped and the machine is checked.


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8.2.3 Analysis: possible failure of the machine

In this paragraph the possible failures of the different components/parts of the machine are stated. The different failure mode are mentioned with their effect and when possible cause are highlighted. This is related to a FMECA (Failure Mode, Effects and Criticality Analysis, www.fmea-fmeca.com), even if different terminology has been used. Complete FMECA analysis is not carried out because information of the occurrence and severity of each failure have not been analysed, being quite difficult to retrieve. This information, in fact, is mainly related to the use of the machine into the plant and thus is an information owned by the plant owner. The case study, instead, has been carried out in collaboration with BARTEC, the machine manufacturer.

Nevertheless the analysis hereafter presented could be even utilized for further FMECA analysis in the future, if needed.

Conveyor

1) Failure mode: Motor failure

Effect: the pallet will not be able to move and the machine will stop.

Protection functions: in case of motor failure the motor protector will cut the power on the motor an alarm will be raised and a message will be shown on the screen.

2) Failure mode: Broken belt

Effect: in case of broken belt the pallet will not be able to move, the machine will stop working.

Check station

3) Failure mode: sensor failure

Effect: if one or more sensors that check the presence of the parts on the pallet are defective there is a mismatch between the information contained on the RFID tag and the sensor data, the part will not be processed.

Protection functions: this error is written on the RFID tag, the robot station will check that the part is present but once the checking has failed, the operator is informed.

Preheating

4) Failure mode: Fan failure

Effect: no air flow is generated.

Protection functions: if the current exceed the threshold the motor


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protector will cut the power of the motor, an alarm is raised and a message is shown on the screen.

5) Failure mode: Heating element failure

Effect: the part will not be heated.

Protection functions: if the heating element breaks down in case of short circuit the circuit breaker will open and a alarm and a message will be raised, in case of open circuit the heating will be ineffective and when the pallet reaches the pre-dispensing position the temperature will be too low and an error will be raised.

Vacuum

6) Failure mode: Motor failure

Effect: the pump is not working.

Protection function: in case of motor failure the motor protector will cut the power on the motor an alarm will be raised and a message will be shown on the screen.

7) Failure mode: Vacuum failure

Effect: if the pump is defective the desired vacuum level cannot be reached.

Protection function: after a timeout an error will be raised.

RFID

8) Failure mode: RFID failure

Effect: the data cannot be read from the tag or are erroneous ones.

Protection function: if the data cannot be read from the tag or when after a writing the data read for check does not correspond to the written one an alarm is raised and the operator has to check the tag.

Dispensing

9) Failure mode: Pump failure

Effect: speed of the pump is different from the expected one.

Protection function: in case the difference between the desired speed of the pump and the actual one exceed a threshold the pump is stopped, the machine stops and the operator is informed.

10) Failure mode: Material empty

Protection function: in case of lack of material the machine will stop and a lamp is lighted

11) Failure mode: Pressure error


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Effect: pressure is too low or too high

Protection function: if during the dispensing the pressure is too low or too high the dispense is stopped, the machine stops and the operator is informed

Comments

Most of the errors during the production are related to the dispensing phase, the wear out of the pumps is the most common error, in this case the output pressure from the pump will be too low and a dispensing error will be raised. The exchange of the component is necessary

8.2.4 Actual maintenance activities on Machine 1

The actual state of the maintenance in the production line of this machine, considering the German plant where this specific machine is installed is the following.

The machines are not connected to any supervising system or customer CMMS, thus no data are collected about the errors/failure of the machine in a centralized way. The log of the error is stored locally in a file on the machine hard disk.

No information about the part, that are processed by the machine, is saved and there is no traceability for the parts, this due to their low cost, if the parts does not pass the quality test are thrown away, because no rework is possible.

To inform the maintenance personnel of the presence of any error or the need of action on the machine the error lamps are replicated on the roof of the building in a position where they are easily visible from any point of the factory. All the machine in the building have their own lamp tower there. For each machine three lamps are present, green to indicate that the machine is running, yellow to indicate that the machine needs refill of material and red to indicate that the machine is stopped.

There are no operators dedicated for the machine, each operator has to follow several machine (the machines are completely automatic, so the operator has to check them periodically)

The maintenance personnel is informed about any problem either looking at the lamps or because they receive a call from the operator.

At this point they have to walk to the machine look at it, identify the problem, fetch the correct tools or spare parts and fix the problem.

Mostly the maintenance on the machine is reactive, apart from the operation carried out every interval as described in the manual, like cleaning or lubricating, as previously mentioned in this chapter.


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9.2.5 Possible maintenance innovations

In this paragraph some possible innovations are discussed. Such kind of innovations could improve the maintenance management for Machine 1. Hereafter there is the description of how the maintenance activities would be if some of the maintenance innovations described before are applied.

First of all the machine should be connected into the plant network, all the information available on the machine should be collected and analyzed by the supervision system of the company. This first of all would allow to have a centralized control of all the machines installed in the plant.

CBM

The condition based maintenance should be introduced, the analysis of the status of the component can be done either via the use of smart sensors and the check of the operating parameters. Nowadays some threshold are evaluated to generate some alarms as explained before in this chapter. Nevertheless once the threshold is overcome, the machine stops. This means that the alarm is only provided for trigger corrective maintenance. A better evaluation of the alarm threshold should be implemented in order to advise maintenance operator with a certain anticipation and allow preventive maintenance, avoiding production stoppages

Failure modes that can be addressed by this technological solution are: 7, 11.

Vibration analysis

Vibration sensors can be placed on the machine on the axis system and the motors of the conveyor, the heating and on the pump, this will help the early identification of some problems. Of course this information would be not useful if not analysed automatically and passed to a central system in the form of information about the status of the machine. This means that more than the installation of some accelerometers, the installation of smart sensors (see also next paragraph) or a more complex diagnostic system based on vibration analysis would be necessary.

In fact, if too much information is collected without automatic elaboration, the risk is that the diagnostic analyses are then too complex or time consuming and thus they become un-efficient.


Smart sensors

The use of smart sensors for the checking of the presence of the part can have the advantage that the sensor itself it is checking its status, if it is defective will communicate its status to the control system and the auto-calibration capability can be useful in case of dirty pallets or parts with different reflectivity.

Electrical signature


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The check of the electrical signature of the machine can be used to identify the insurgence of mechanical problems on the motors, the axis, the pumps and to check the status of the heating elements.

Since many parts of the machine move/work simultaneously, it could be difficult to precisely isolate which component is failing, starting from the electrical signature of the entire machine. To this end a cycle test, properly designed for diagnostic purposes would be advisable if/when electrical signature analysis is applied.

This would mean that periodically (e.g. one time per day or one time per shift) a quick cycle test should be run. The cycle test could last 1 minute, thus it should not affect the availability of the machine for production. Nevertheless this way of applying Electrical signature analysis could only check failures that evolve in a medium term (days). In fact, if a failure starts during the day, this diagnostic approach can be able to detect it only during the next diagnostic test.

An appropriate engineering of this approach is thus advisable.

Failure modes that can be addressed by this technological solution are: 1, 4, 5, 6, 9.

Process parameter analysis

The analysis of the process parameter can allow the identification of the presence of wearing out characteristics. The speed of the pump, its current (and thus the torque), the outlet pressure of the pump can be monitored and any variation of these parameter will help to identify the future condition of the component. This seem one very feasible solution that does not require installation of sensor, nor complex engineering and development of diagnostic tests.

Failure modes that can be addressed by this technological solution are: 2, 3, 7, 8, 10.

PDA

With the use of a PDA with proper maintenance software, the maintenance personnel does not have to wait the call of the operator or look at the status lamps of the machines of the line but he can be informed by the CMMS system on his handheld device of the presence of a failure or of a forecast of a failure on a machine. The system can give him all the information he needs, about which part he has to check, help him to schedule the activities according to their seriousness and the importance of the machine. The management of spare parts can also benefit from this. The availability of spare parts in the warehouse could be checked by the handheld device, helping the maintenance operator in the preparation of the maintenance task.

When the operator reaches the machine he can have access at the manuals and the maintenance procedures on his device without wasting time to look around for the paper copies.


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The list of the spare part and tool needed and their position can be checked on the PDA, there will be no more need to wander around looking for the needed parts.

The PDA can be useful during the visual inspections, pictures of the various part of the machine can be taken and then compared with the previous one to look for signs of wear out.

8.2.6 Why these solution are not applied

In this paragraph there is a description of reasons why the innovation presented before are not applied on this machine.

CMMS

The lack of a supervising system of the customer does not allow the possibility to connect all the machines to a central system and control them from there. The reasons of the non adoption of this system are several.

First of all there is the economic investment to consider, in this case the investment to set up a supervising system and control all the machines in the plant can be of the comparable to the price of the machine. Second the customer will not be able alone to identify which parameters are worth checking, how to identify the state of the machine and the list of the errors, so it is necessary a deep collaboration with the supplier of the machine that can be either not keen to open his system to other or will charge the cost for this development.

Indeed the main reason of non adoption of such system lies on the complex relation between the plant owner and the providers of the machines that are also involved in the maintenance activities, through their maintenance service.

Vibration sensors

The installation of vibration sensors all over the machine can be done by the customer but again the cost of the sensors, the installation and the periodic check exceed the cost of the component monitored and since the parts are quite inexpensive and small a stop of the production does not have a great economic impact. Thanks to the small size of the part a safety stock can be easily stocked in the warehouse to face any production stop and the presence of several similar machines in the line can allow the customer to reroute the production of the stopped machine to the other.


The use of the electrical signature of the machine to identify the possible presence of problem can be interesting but again there is the economic factor to consider. Checking the electrical signature of the complete machine during operation is not possible because there is not an identifiable cycle, the presence of several pallets


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on the machine that can move independently, the possibility to have a different number of parts on the pallet and the fact that several process are done in parallel lead to the fact that the electrical signature is not clearly identifiable. On the other way checking the electrical signature of each component require much more sensors and, thus, it is expensive. The remaining option is to create a test cycle, in this case the components are activated always in the same order and so it is possible to carry out a comparison between two different measurements. Beside this it will require also a lot of computational power and storage space to save all these information and process them, this cannot clearly be done on the PLC due to its limitations.

Smart sensors

Smart sensors are not used for mainly four reasons.

First the cost: a smart sensor cost at least the double of the normal one and the additional information that it provides are not so important in this case.

Second, the supplier is not completely free to choose which component he has to put on the machine, the customer provide a list of component that should be used because he has them on other machines in the same plant and so he can have only one spare part in stock instead of one of each type of sensor with the same function (even if the function is the same and the electrical connection are the same the response of the sensor of different supplier can be different).

Third, the smart sensors are not widely used, the sensor producers have not started to push on this technology and especially the biggest sensor producers does not have in catalogue many smart sensors.

Fourth, while a normal sensor does not need anything more than a standard input the smart sensors require a bus network that is not present on the machine and especially if the sensors are ethernet based at the moment there is no support from the plc supplier for an easy integration of the sensors in the program while there will be no issue with a standard one.

Thus basically these four reasons are related to the effort of smart sensors producers to provide flexible solutions and push the market of these devices. If the market of this kind of solution arises, also the machine manufacturer will start to think about the integration of such solution, thus allowing less problem of introduction of these devices on the machines.

Process parameter

The monitoring of the process parameter can be done inside the plc but to achieve good results and avoid false alarms loads of data are required to identify a pattern, the more data are available the more the prediction can be accurate but at the same time more computational power is required to handle the data and more space on the hard disk is required to store the information, the flash disk of the machine is not designed to have high capacity neither to write continuously so it will be


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necessary to store the information on a different media.

Indeed this solution is the one that seems more feasible to be implemented, since few technical issues are against to it. Moreover it seems that the machine manufacturer could also provide a diagnostic functionality starting from this.

One main issue preventing from this implementation is then the availability of data to train the diagnostic system. These data should be retrieved from the customer/plant owner that could be against the delivering of maintenance information of their own machines/production plant.

PDA

The use of handheld devices to support the maintenance personnel during their tasks is not widespread among the industries, at the moment there are not available many models that have the characteristics necessaries to be used in an industrial environment and the available ones are too expensive and heavy and the absence of a computer system where all the information are stored made this solution not so useful.

The main issue preventing this adoption is thus the lack of a provider of solution for maintenance for PDA that can make easier the adoption of such solution for a company. Indeed if a company should develop a mobile maintenance solution by itself it would result in a too expensive investment, thus not feasible.

8.2.7 Possible improvements

Beside the fact that many innovations claimed by literature cannot be easily applied to this case, some possible improvements are possible and are hereafter listed.

A.

A software counter for the running time of the most delicate components can be implemented quite easily. This is the first step for the implementation of the time based maintenance. The machine is counting the time that a part is working, when the working time exceeds the defined threshold a warning is triggered, this warning can be notified to the user either with a message on the screen or through an e-mail sent to the maintenance department. In this case the maintenance is done according to the usage of the parts and not after a fixed time. The state of the part in case of exchange or repair can be checked and this information can be used to set a more accurate time interval for the exchange.

This seems an interesting solution that could address Failure mode number 1, 2, 4, 5, 6, 7, 9.


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B.

Another possible improvement is the monitoring of the process parameters, at the moment the pump pressure bounds are checked and error are raised in case the pressure is out of the bounds, similar reasoning for the pump. To compensate the wear out of the pump its speed is increased, it is possible to correlate pressure and speed and use this information to identify the health status of the pump. Due to the limited memory available the historical data cannot be much and the sampling of the parameter cannot be continuous but it should be possible to find a compromise between the accuracy of the monitoring and the computational power required.

This seems an interesting solution that could address Failure mode number 9, 11.

C.

A simplified electrical signature can be implemented current sensors can be placed on the main line and can be used to check the current consumption of the components. Due to the fact the cycle is not always the same because the pallet are able to move independently it is not possible to analyze easily the current during the normal operation but it is possible to implement a simple procedure to test the various parts, the motors, pumps and heating can be switched on separately, the consumption of the machine is measured and this value is compared to the previous or the reference one to identify any discordance and thus a sign of degradation or problem.

This seems an interesting solution that could address Failure mode number 1, 4, 5, 6, 9.


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8.3 Machine 2

The second example is a machine used for the impregnation of the electrical motors for professional power tools, the aim of this process is to increase the electrical insulation and mechanical characteristics of the parts.

8.3.1 Machine description

The machine is mainly composed of a conveyor with a cooling section, a checking station, an automatic load/unload station and the main part of the machine that is composed of oven and a dispensing zone.

The conveyor is a closed loop one, on the conveyor are running several pallet and each pallet has a support for two parts, after the parts have been processed they are unloaded from the machine and putted on the pallet, the pallet is going to the cooling section, after a desired time in the cooling section the pallet is released in the unloading zone where the operator takes the part from the pallets and put the pallet on the next machine. When the sensors see that the parts are no longer on the pallet this one is released and goes to the loading zone where the operator put new parts on the pallet. After the loading operation, the pallet moves to the checking zone, here the camera checks 7 measures of the part and check if there is a match between the type inserted by the operator and the actual part and if the parts are of the same type.

The machine is composed of a big chain that holds the chucks where the parts are loaded. The parts are kept in rotation on their axis to ensure a correct distribution of the resin over the part. This chain goes through four zones, the preheating oven where the parts are heated up to facilitate the penetration of the resin into the small gaps between the wires, the dispensing zone, the curing oven and the load/unload zone.

The ovens are mainly two closed chamber with heating element and fans to ensure an equal temperature all over the oven.

The dispensing zone is divided into 4 zones, the first one is for the gel coating of the commutator. Then there is a waiting zone and then two successive zones to dispense the resin over the winding. There are 5 static mixing head, for each one there are 2 pumps, one for each of the two component of the resin. Every mixing head has a nozzle to dispense the resin on the correct position on the part. The nozzle is moved on the correct position with a linear axis, in total there are 5 axes.

The load/unload station had the job to take the parts that are vertical over the pallet and load or unload them on the chucks that are horizontal. A vertical axis will lift the pallet at the correct height and the trolley will take them, turn them


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and load them on the machine.

The process is the following, the machine has a cycle time set by the time that the resin over the parts have to stay in the oven to cure completely, every time this cycle time is elapsed the chain makes one step forward, the parts loaded on the previous cycle go into the preheating oven and two parts came out from the curing oven. The trolley download them from the machine and put them on the pallet, the pallet is then released and goes to the cooling section of the conveyor where the parts are cooled down. When the loading zone is empty the pallet from the checking station is released and it goes into the loading station, it is lifted up and the trolley takes the parts from the pallet and loads them on the machine. Meanwhile the parts in the dispensing zone are dispensed.

The information about the status or the parts that are in the machine is inside the PLC, if there is any error during the processing of the parts this information will be stored and when the part is unloaded from the machine a message and a sound will inform the operator. The machine uses an encoder to know which chuck and which part are in a desired position.

This machine is equipped with a Siemens PLC; an Ethernet port is present, is used for the communication with the camera and to make a backup or restore the recipes. The communication capabilities are very limited as is the memory, only 256kB are available for the entire program and the data.

The machine considered for this case is installed in a production plant in Germany.

Figure 21: Top view of machine 2


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The main components of the machine are:

1. Electrical cabinet

2. Preheating oven

3. Dispensing zone

4. Curing oven

5. Load/unload trolley

6. Conveyor

7. Cooling

8. Camera zone

Figure 22: Lateral view of machine 2


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Figure 23: Side view of machine 2

Figure 24: Camera picture for piece measurement


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8.3.2 Prescribed maintenance

For this machine we can divide the prescribed maintenance in two parts.

The first one is usually carried out by the production operator, no tools or particular knowledge are required, normally the operator can do these operations before he starts the working shift or before leaving the machine in stand-by.

The second one is carried out by the maintenance personnel or by the manufacturer personnel, namely the maintenance service personnel of the manufacturer. These maintenance actions require tools and mechanical or electrical knowledge.

Daily

Element Action

Lubrication oil - Check the filling level

Compressed air circuit - Check pressure and leakages

Oil and resin dip tray - Check the filling level

- Properly remove the waste

Weekly

Element Action

Gelling and curing over - Clean the drops of resin

Load/unload area - Clean

Monthly

Element Action

Trolley grippers - Cleaning and check

Machine chucks - Cleaning and greasing

Safety devices - Check functioning

Gearboxes - Check oil level

- Refill if necessary

Oil distributing tubes - Check the position

Dust filters for the cooling units

- Clean or replace

Table 5: Prescribed maintenance for machine 2


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The maintenance actions described before can be done by the production operator. The following table, instead, describes the maintenance tasks that are more complex and thus require more skilled operator.

Element Scheduled maintenance

Scheduled exchange Action

Main Chain 6 months 60 months Cleaning, allowances check

Parts Rotation chain 6 months 34 months Cleaning, check if there are broken roller

Machine chucks 6 months 34 months Cleaning, lubrication, spring change

Pallets 6 months 34 months Cleaning, change pieces holder

Front chain slide 6 months 34 months Cleaning, check wear level

Pt100 probe for ovens 12 months 60 months Calibration check

Thermoregulator device for ovens 12 months 60 months Calibration check

Heating elements 3,5KW 6 months 34 months Cleaning, check status

Infrared heating elements 3 months 20 months Cleaning, check status

Proximities diameter 18 6 months 34 months Cleaning, check status

Load/unload trolley chuck (piston) 3 months 20 months Cleaning, check status

Load/unload trolley chuck (grippers) 6 months 34 months Check alignment

Load/unload trolley (Drive Belt) 6 months 34 months Check the tension

Conveyor belt (Flexlink) 6 months 34 months Cleaning, check tension

Conveyor slides (Flexlink) 6 months 34 months Cleaning, check tension

Motor fans for heating 6 months 34 months Clean cooling fans


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oven and tunnels

Fans for heating oven and tunnels 6 months 34 months

Clean fans and check for vibration

Suction fans 12 months 34 months Clean fans

Drippers positioning systems (screws) 3 months 34 months Cleaning, lubrication.

Drippers positioning systems (motors) 12 months 60 months Cleaning, lubrication.

Ovens doors barriers 3 months 34 months Cleaning, lubrication of slides

Ovens, Tunnels doors gaskets 6 months 34 months Cleaning, lubrication.

Cylinder to open chucks 6 months 34 months Verification of stroke

Pressors shaft to open chucks 6 months 20 months Verification of stroke

Gear Boxes 12 months 60 months Check the oil level

Main Shafts Bearings (rotation and transfer) 6 months 34 months Check, lubrication

Cylinder to chain centering 6 months 34 months Check

Encoder driving joint 6 months 34 months Integrity check

Video camera light system 6 months 34 months Integrity check

Pallets lifter systems (screws) 3 months 34 months Cleaning, lubrication

Pallets lifter systems (motors) 6 months 34 months Cleaning, lubrication

Solid state relay for Heating (3Phase) 12 months 60 months Check operation

Solid state relay for Heating (1Phase) 12 months 60 months Check operation

Table 6: Scheduled part maintenance/exchange

The times of the previous table are calculated according to the experience on previous similar machine.


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8.3.3 Possible failures

Here are stated the possible failures of the machine, according to the same structure used in the previous paragraph.

Oven

1) Failure mode: Cannot reach the temperature

Cause: if the oven cannot reach the desired temperature this is due to problem of the heating elements.

Protection functions: the machine cannot start the production if the temperatures are not in the prescribed range.

2) Failure mode: Over temperature:

Cause: this problem is related to the probe. If the probe is in a wrong position or it is damaged then the read temperature is different from the real one.

Protection functions: if the oven temperature exceed the safety limit a hardware safety cut the power to the heating elements

3) Failure mode: Fan noise

Cause: multiple causes are possible: if the impeller is bended, unbalanced or damaged the vibration of the fan increases and so the noise due to vibration or friction is audible.

4) Failure mode: No power on the fan

Protection functions: if the motor current requirement exceeds the normal value due to the increase of torque needed because of wearing out or friction the motor protector cut the power to the motor.

5) Failure mode: Heating element short circuit

Protection functions: if the heating element breaks and goes in short circuit the circuit breaker cut the power to it.

6) Failure mode: Heating element open circuit

Effect: if the heating element breaks and became an open circuit there is no current running through it, the result is that the time to heat up the oven increases because total power is reduced, this problem can be identified checking the time to reach the desired temperature or with the current check procedure.

Dispensing

7) Failure mode: Axis blocked

Effect: if the axis is blocked the motor goes in fault.


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Cause: this can be caused either by a mechanical problem or also by cured resin oven the screw of the axis.

8) Failure mode: No power on the axis

Cause: this happens because the motor cannot turn because it has no power that is due to the circuit breaker that has been triggered.

9) Failure mode: Pump pressure too high

Cause: a too high pressure at the output of the pump can be an indication of problems in the tubes, the valves or the mixing element. The tube can be blocked or bended and the valve or mixing element can be blocked by the cured material inside.

10) Failure mode: Pump pressure too low

Cause: if the outlet pressure is too low this can be an indication of problems of the pumps or a broken tube.

11) Failure mode: Pump speed out of tolerance

Cause: if the actual pump speed is out from the tolerance this is an indication of problems of the pump.

12) Failure mode: Air bubbles

Cause: air bubbles in the material line are indication of lack of material.

Protection function: the operator is informed about the low level of the vessels.

Trolley

13) Failure mode: Gripper loses part

Cause: If the gripper loses the part this is an indication of a problem of the gripper or a defective part

Protection function: when the gripper closes over the part and the trolley is moved away the machine checks if the part is in the gripper or not, if the part is lost the operator is informed.

14) Failure mode: Machine loses part

Cause: if the machine chuck loses the part this is an indication of a problem of the chuck

Protection function: when the parts are loaded on the machine or removed from it the presence of the part is checked. The machine checks this condition and if the parts lost counter of a chuck continues to lose the part the operator is informed about this fault.

Conveyor

15) Failure mode: Conveyor belt not running


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Cause: if the conveyor belt is not running that means that the motor protector was triggered and it has cut the power to the motor due to the excessive current consumption.

Camera

16) Failure mode: Error in the identification

Cause: if the camera cannot identify the parts or if the part does not match the recipe the operator is informed and he has to check what is wrong. There are several reason for an error in the identification:

-the parts can be placed wrongly on the supports on the pallet

-the pallet can be dirty with oil or dirt that can cause a wrong identification of the edges

-the lens of the camera can be dirty

-some of the led of the backlight can be dead

Protection function: The operator can connect with a PC to the camera and check the reason of the error.

Cooling

17) Failure mode: No cooling

Cause: if the cooling unit is not working that means that the circuit breaker was triggered by an abnormal current consumption.

.

8.3.4 Actual maintenance status

Actually the maintenance of the machine is mainly a reactive or time based one. The machine error lamps are replicated on the roof of the plant together with the ones of all the other machines. If the machine is running the green lamp is on, if it needs material refilling the yellow lamp is lighted and in case of error the red one is on. The maintenance personnel can either be informed of a fault by the operator or look at the lamp chart.

An operator is working on the line, but is not dedicated on a particular machine but he has to control all the machines in the line. Every time the machine needs the attention of the operator an acoustic alarm is generated, there are different sounds for different messages.

The machine is not connected to any supervising system and the customer has no CMMS, all the information about the production rate, errors or notes are written down on a paper spreadsheet placed near the machine.

Before the start of the working shift the operator meets the other operator of the


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previous shift, information about the errors, problems and changes are exchanged verbally.

The machine counts the time that the various parts have worked and when the time since the last maintenance action is elapsed the machine signals to the operator that some maintenance actions are required. To reset this message and restart the timer the maintenance operator has to log in on the machine with his password (according to the level of the login different pages or options are available). The operator can then confirm that the task has been carried out.

Some maintenance actions are carried out daily by the operator like the cleaning and the exchanging of the mixing tubes and the cleaning of the dispensing zone. During this checks the operator does also a visual inspection of the screw of the axis and the nozzles.

During the production the operator has to confirm every time that a different type of part is arriving in the dispensing zone that the part is the correct one and after this he has to check the dispensing position and the quantity of the resin is correct. This confirmation ensures that the correct parameters are loaded and that any error or modification of the parameters has to be checked by the operator.

On the machine is mounted an energy analyzer, this is just and indicator, it is not connected to the PLC, every 6 month the maintenance personnel has to do a diagnostic procedure to check if the current consumption of the various components of the machine. The machine guides the operator through this operation, the procedure consists into switching on in sequence the various part of the machine, the operator has to read on the instrument the actual current consumption and on the screen are shown the minimum and maximum value acceptable, if the operator confirms that the values are ok the machine will switch on the next component otherwise the procedure is aborted. This procedure is useful to individuate the presence of faulty heating elements.

When a part is taken from the pallet or loaded/unloaded from the chucks sensors will check if the part is correctly in position or if it has fall down. This check is used to identify problems on the chucks, the last 16 cycles are checked by the machine and if the number of lost parts exceeds the threshold an error is generated. According to this information the operator can mark the chuck as defective in the software and the machine will not load parts on it. The machine knows the position of the chucks thank to the use of an encoder.

8.3.5 Possible maintenance innovations

In this paragraph the possible implementation of the innovations in maintenance are presented for this type of machine.


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The machine can be connected to a supervising system, since the machine is equipped with a Siemens PLC. The possible protocols are only the proprietary ones. With the use of a supervising system it will be possible to log the events and error of the machine to have a better understanding of the machine behavior.

CBM

Generally a condition based maintenance policy should be used to allow a better usage of the components, at the moment the components are changed either when the part has failed or preventively on time based, in both cases there is an economic waste because in the first case the production is stopped and for the second one the part that can still be used is changed.


Vibration monitoring

Vibration monitoring can be used to identify unbalanced fans, the fans motor are not easily accessible so the best would be to have sensors placed on every motor, this will allow a continuous check of the state of the motor and the fan.

Failure modes that can be addressed by this technological solution are: 3

Another solution will be the possibility to check the state of the components on interval basis, the maintenance operator can connect the sensors to the motor and check the vibration reading them through its PDA. In both cases the analysis of the signals has to be done by a PC, the PLC does not have the computational power and the memory to do this task.

Process parameter

The check of the process parameter will allow the machine to identify the insurgence of failures. The checking of the time to reach the temperature or the output power of the thermoregulator will give the information about the state of the heating elements. The correlation between desired pump speed, actual speed and pressure can be used to state the status of the components.



At the moment there is not an electrical signature analysis, the check is done periodically by the operator. With the electrical signature it will be possible to identify the failures or the degradation of the component. The power consumption of the motor and heating elements can give a clear idea about the state of the components.

Failure modes that can be addressed by this technological solution are: 1, 4, 5, 6, 8, 15, 17.

Smart sensor

At the moment the only smart sensor used is the camera that is used for the


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measuring of the parts. Smart sensors can be placed on the machine to acquire more information and thank to their capability to acquire and process the signal they can be used to help the PLC in its task.

CMMS

The use of the CMMS will help the scheduling of the maintenance tasks. The problem, errors and modification can be inserted into the system, changes will be tracked and with the information about which operator has made the change and why. The maintenance personnel does not need to check the lamps or the operator does not have to call in case of error but the system will recognize the error and dispatch this information to the correct person giving him all the information that he needs to fix the problem. The current status of the machine can be identified by the system, the maintenance task schedule can be adjusted according to the real necessity and not like now with the logic of the first arrived first served.

PDA

The maintenance personnel can have a PDA, the use of this device associated with the use of a CMMS will help the worker to receive the information about the status of the machine and during the maintenance tasks he can use it as a support during the execution of the tasks.

8.3.6 Why these solution are not applied

The company does not use a CMMS, one of the main reason of the non adoption of the computerized system is the fact that most of the operation in the plant are manual, the only automate process are the impregnation and the winding line so the system will only be used for these two lines. Three maintenance operator are present on the line, they will cover the two normal production shift. The main job of the maintenance workers are the setup of the winding machine after the change of production the exchange of the copper wires and the fixing of the problem like jamming or similar problem. If they do not have anything to do, they will help the operator during the production.

Problems like jamming, broken winding wires are not easily identifiable and there is no identifiable pattern before the fail so the CMMS cannot help much in these cases.

Even with the use of the CMMS the number of the maintenance personnel cannot be reduced because at least one of worker is necessary to take care of the lines.

Vibration monitoring

Thanks to the analysis of the vibration of the motors it is possible identify the insurgence of problems, in this case this analysis is not used because the cost of the sensors can be compared to the cost of the motor, the motor is an


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asynchronous with squirrel cage rotor thus very robust and is slightly bigger than necessary so is not under strain, the propeller of the fan is balanced and there are no axial forces on the shaft so it can work in the best conditions and is life time is bigger than the machine’s one. There have been no claim from the previous customer and there are machines that are running since 20 years with the original motors and fans so the expense is not justified. In case of failure the motor and the relative heating element can be switched off and the machine can continue to work (it will take only more time to heat up the machine in the morning) while the spare parts are ordered.


A simplified electrical signature check is done on machine with a special programmed function, in this case the machine is not running in normal mode and not all the components are switched on but just fans, conveyor, heating and cooling devices. A more exhaustive one can be done only by adding more current measuring sensors and checking the current consumption of all the most important components during the normal production, this will require a more powerful processor to analyze the signals. Normally this advanced check is not required because there is a simpler way to detect a failure, if there is a degradation of the components and the current increase the motor protector or the circuit breaker are triggered, if this happen too often this is an indicator of a problem of the component, there are no sensors and the machine can continue to work, it is enough to start again the component and set an higher intervention threshold while the new component is ordered.

Smart sensors

The smart sensor in this machine are not used because their advantages are not necessary, there is no need of autodiagnostic because in case the sensor is broken the machine will identify the fault thanks to timeouts or through the diagnostic of the input terminals, it is quite easily accessible for the exchange and also the autocalibration function will not be used. Apart from this the communication capabilities of the PLC are small, the maximum number of nodes that can be connected to a profibus line is 126. In ethernet the PLC can handle only 8 or 16 connection, so the number of smart sensor that is possible to connect is very small.

Process parameter

Process parameter are checked even if in a simple way, for example the bounds of the resin pressure and of the pump speed are checked but there is no correlation between so some useful information are not processed. Also during the movement of the machine the time for the movement is checked but only as a timeout, there is no historical data to check if there is a degradation of the performances. The main reason of this is the fact that on the PLC there is not enough space to save historical data about all the events or movement, for this application it would be


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required a complex supervision system that has to collect all the data from the PLC, store and analyze them. The cost of a supervision system like this can have a price of half of the machine price.

PDA

The use of the PDA it is really useful on big plants or when the machine are very big or distant, in a small production plant like the one analysed for this case study on Machine 2, the maintenance personnel can be either called by voice or he can listen to the acoustic alarm of the machines. Thanks to the fact that the machines make different sounds to signal a warning, an error or just the fact that a confirmation from the operator is required, the operators can immediately understand if it necessary an intervention or not. Also the use of the PDA during the maintenance actions is not so helpful because the actions carried out on the machine are always the same and they are well known, the most difficult and not common operation are done by the personnel of the maintenance service of the producer of the machine that usually knows the machine better.

8.3.7 More suitable innovation

In this paragraph the possible changes on the machine are explained, these innovation are all easily applicable and not too expensive (necessary requirements for the application in a real contest).

A.

It is possible to improve the diagnostic capabilities of the machine, a simple supervision system can be installed to record all the events and alarms from the machine, this is useful to understand the error and its causes. The process parameter check can be improved, even if it is not possible to use the trend analysis a minimum correlation between different but related parameters can be introduced.

This seems an interesting solution that could address Failure mode number 1, 9, 10, 11,16.

B.

It is possible to enhance the electrical check in two ways. At the moment the components are switched on in groups according to their functions, for example all the fans in the preheating oven are switched on together, it is possible to separate the commands of the components so it will be possible to switch them on independently and have a more precise reading and identify the faulty component


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easily. The second improvement possible is to connect the current measuring sensors to the PLC in this way the check procedure can be done automatically without the aid of the operator, this will increase the quality of the check because the test condition will be more constant and there will be no errors.

This seems an interesting solution that could address Failure mode number 1, 4, 5, 6, 7, 15, 17.

C.

At the moment the information about the result of the process of the parts is lost after the unloading, a message is shown on the screen and the operator has to acknowledge it to continue the unloading, but the part is not marked so it cannot be identified after it goes into the cooling tunnel. This information can be stored on an RFID tag over the pallet and then used to separate the good and the bad part before the unloading zone. It is enough to put three RFID reading/writing head in the machine and a RFID tag on all the pallets.

D.

At the moment there is no easy way to check the cause of the error in the identification of the parts by the camera, the operator has to take the maintenance notebook and connect it to the machine, start the program and analyze the fault. If a supervision system is available it can collect the pictures of the camera, check recurrent failures (of the operator or dirty pallets) and use this information to solve the problem and prevent it.

This seems an interesting solution that could address Failure mode number 15.


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Cap. 9 Conclusion

Some of the reasons why advanced maintenance technologies have not been well implemented in industry might be (Jardine et al., 2006):

• lack of data due to incorrect data collecting approach, or even no data collection and/or data storage at all

• lack of efficient communication between theory developers and practitioners in the area of reliability and maintenance

• lack of efficient validation approaches

• difficulty of implementation due to frequent change of design technologies, business policies and management executives

These problems are also visible analyzing the case studies, methods to improve the maintenance are available and usable but they are not implemented for a cost reason.

Engineers are always looking for ways to reduce production system down time and increasing availability and guarantee the reliability. The ultimate objective of maintenance is to maintain the system functionality to the maximum extent possible with optimum tradeoffs between the down times and cost of maintenance, avoiding any hazardous failures. Inexistence of proper computerized maintenance system, lack of competences to properly handle maintenance data, or reduced knowledge in advanced maintenance processing techniques, are common problems that have to be solved in order to benefit from the historical record of failures and maintenance operations carried out at certain equipment.

Moreover, since industrial systems evolve rapidly the maintenance concept will also have to be reviewed periodically in order to take into account the changing systems and the changing environment. This calls not only for a structured, but also for a flexible maintenance concept, allowing feedback and improvement.

To decide which component or plant requires a modification of its maintenance strategy it is necessary to observe and analyze the machine performance data, such as (Mobley, 2002)

• Frequency of breakdowns

• Randomness of breakdowns

• Need for repetitive repairs

• Number of defective products produced

• Potential dangers linked to poor performance

• Any excessive fuel consumption during operation


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• Any reduced throughput during operation

From these information it is possible to identify the components that requires either careful monitoring, routine planned preventive maintenance, different repair procedures to achieve a reasonable availability level. Very few companies have an idea of the cost of downtime per hour. Many have no reliable records of their aggregate downtime at all, even if they could put a value per hour on it. (Mobley, 2002).

As regards human resources highly qualified personnel will be required and the continuous training is needed, especially because the computer support will spread in all the part of the companies for the various task like stock tracking, personnel management, job order tracking, processing of historical data, efficient document control and so on.

From the case studies analyzed and in the surveys result is possible to see that the maintenance personnel has a good knowledge about the mechanical and electrical problems while the knowledge of the computer systems are more limited.

The most critical factors for the success of an efficient and effective maintenance approach are:

• deep knowledge of maintenance technology: the direct production personnel as well as the maintenance workers need knowledge and competence to prevent disruptions at an early stage of the production process

• management skills regarding planning and control of maintenance tasks as well as Human Resources Management (HRM): studies have shown that long-term maintenance plans, company-wide maintenance knowledge and participation of manufacturing personnel in the planning of maintenance are of major importance

• flexibility to exploit opportunities and trends, such as the expanding maintenance services market and the opportunities offered by ICT.

In the development stage of a maintenance plan, data is one of the most important requirements, and gathering it is one of the most difficult jobs.

The performance and competitiveness of manufacturing companies is dependent on the reliability, availability and productivity of their production facilities. To ensure the plant achieves the desired performance, maintenance managers need a good track of performance on maintenance process and maintenance results

Deterioration of manufacturing systems’ condition, and hence its capability, begins to take place as soon as the system is commissioned. In addition to normal ear and deterioration, other failures may occur especially when the equipments are pushed beyond their design limits or due to operational errors. As a result, equipment downtime, quality problems, safety hazards or environmental pollution become the obvious outcomes. All these outcomes have the potential to impact


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negatively the operating cost, profitability, customers’ demand satisfaction, and productivity among other important performance requirements. To ensure the plant operates at the required condition while meeting its production targets at an optimal cost, maintenance management has to make conscious decisions regarding the maintenance objectives and strategies that need to be pursued.

Once the objectives and strategies have been established, the success of the maintenance function is dependent on the maintenance work management

The maintenance work management cycle, as outlined by Campbell (1995), consists of work identification, planning, scheduling, execution and closing the job. Maintenance work is identified from the preventive, predictive and failure finding work orders that are usually generated by proactive maintenance. Repair work arises as a result of failure.


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