Prognosis Monitoring System: methods and application cases

Prognosis Monitoring System: methods and application cases

Mechanical and Mechatronics Systems Research Laboratories (MMSL)

Hung-Tsai Wu ([email protected])

2017.05.02

Copyright 2017 ITRI 工業技術研究院 2

Mechanical and Mechatronics Systems Research Laboratories (MMSL)

A total of 7 divisions, 26 departments, 506 staffs (Ph.D. 19%,MS 46%)

MMSL

Vehicle homologation

Advanced Manufacturing Technology

Intelligent Robotics Technology

Industry IoT Technology

Intelligent Mobility Technology

Controller Kernel Technology

Advanced Machinery Technology


About us – Prognosis & Decision Tech. Dept.Starting from vibration signal analysis, the Prognosis & Decision Tech.Dept. has been engaged in rotating machinery vibration monitoring, faultdiagnosis, modal analysis, and structural analysis for more than 20 years,and has served more than 100 different companies, including machinetools, metal working, plant equipment, nuclear plants, and robotics.


What is “Prognosis” ?• Run to failure

• No maintenance is performed on the asset until the failure event• Used where the economical impact of breakdown is none or

minimum• The most expensive method (including inventory costs)

• Time-based/Preventative Maintenance• Service intervals specficied by OEM/experienced staffs• Maintenance is performed whether the equipment needs it or not• Does not identify problems that develop between the scheduled

inspections

• Predictive/Condition-Based Maintenance• Maintenance is only performed when the machinery needs it• The skill level, knowledge and experience required to accurately

interpret condition monitoring data can be high• Cost saving by minimizing (un)scheduled downtime and thus

increasing production

The E

volution of Maintenance Strategies


What is “Prognosis” ? (cont.)

• Q1: there should not be any components in this quadrant because such issuesshould have been noticed and fixed during the design stage

• Q2: should have an adequate number of spare parts on hand• Q3: the current maintenance practices are working for these components and

no changes are required• Q4: here lies the most critical components, thus predictive maintenance

should be employed*J. Lee et al., Prognostics and health management design for rotary machinery systems — Reviews, methodology and applications,Mechanical Systems and Signal Processing, 42 (2014), 314–334


Prognosis Monitoring System (PMS)• Needs

• Health condition monitoring of rotating machinery is essential to bothmakers and users, but the current solution of time-based or preventativemaintenance are mostly performed off-line and regardless of needs.

• Solutions• Three core modules/algorithms are developed for predictive maintenance of

various types of rotating machinery.• The performance assessment module is capable of evaluate the health status.• The trend prediction module is used to indicate possible changes of the

health status.• The fault diagnosis module is constructed according to domain expert

knowledge.

• Benefits• By employing the PMS, the users can benefit from on-line health condition

monitoring, accurate fault localization, cost saving, and so on.• Maintenance is only performed when the machinery needs it, so most of the

unscheduled/unwanted downtime can be eliminated.


Prognosis Monitoring System (PMS) (cont.)

Performance Assessment ModuleUsing time/freq. domain feature extraction and regression algorithms, and takinginto consideration the characteristics of the equipment and correspondinginternational standards, the performance assessment module can be used toperform on-line health status monitoring. Users can also specify the monitoringitems and thresholds, such as the amount of vibration, specific spectral sub-bandcomponents, etc..



Trend Prediction ModuleFor a better prediction accuracy, data smoothing is firstly conducted on thehistorical data of the performance assessment module, afterwards time-seriesprediction algorithms are employed for predicting when will the equipment’sperformance reaches a customized maintenance point. This could help in lengthenthe MTTF, and approaching near-zero downtime.



Fault Diagnosis ModuleUsing time/freq. domain feature extraction and fuzzy neural network learning, andtaking into consideration the characteristics of the equipment and expertexperiences, the fault diagnosis module can be used to identify more than 20 typesof faults regarding critical components such as shaft, bearing, gear box, and motor.This could help in avoid misjudgment made by the staffs, and shorten the MTTR.


Application Scenario


Use Cases• Machine Tools (Maker)

• Needs: All the machining centers must be tested with calibrationartifacts before they can be delivered.

• Current Solution: Exterior observation of the calibration artifacts by thestaffs, or using vibration meter to make go or no-go decisions.

• Expected Solution: Employ the performance assessment module of PMSfor grading the health status of the machine.


Use Cases (cont.)

• Machine Tools (Maker)• Needs: Gathering the performance and fault diagnosis data for better

after-sales service and for a reference of the development of newmachine tools.

• Current Solution: On site inspection by the staffs.• Expected Solution: Employ the fault diagnosis module of PMS.


Use Cases (cont.)

• Grinders (User)• Needs: Integrate performance monitoring and diagnosis functions for

the development of next-generation equipment.• Solution: Employ PMS for grinder spindles.


Use Cases (cont.)

• Nuclear Plants• Needs: Vibration monitoring systems for recirculation pump.• Solution: Employ PMS for remote monitoring, where the signal is

collected and processed in ITRI. The signal condition module was doneby ourselves.


Use Cases (cont.)

• Wind Turbine• Needs: Remote vibration monitoring, life cycle prediction, and fault

diagnosis.• Solution: Employ PMS for remote monitoring.

Copyright 2017 ITRI 工業技術研究院

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Prognosis Monitoring System: methods and application cases

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