A DIAGNOSTIC MAINTENANCE SYSTEM - UNSW Canberra · 2018-05-11 · SYDNEY HARBOUR, NSW 5. ......

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SUPERVISORS: Associate Professor Jonathan Binns, Professor Kiril Tenekedjiev, Dr. Rouzbeh Abbassi, Dr. Vikram Garaniya, Michael Lonsdale

A DIAGNOSTIC MAINTENANCE SYSTEMFOR COMMERICIAL AND NAVAL VESSELSJANE CULLUMjane.cullum@utas.edu.au

HMAS SIRIUSCAPTAIN COOK GRAVING DOCK, NSW, 2014 HMAS SIRIUS AND HMAS MELBOURNESOUTH CHINA SEA, 2017

1. Periodic planned maintenance and RCM are not optimal but work

2. Limited data andknowledge of how to interpret it

3. No need for innovation?

4. Applications?

COMMERCIAL AND NAVAL VESSEL MAINTENANCE: State-of-the-art

CHALLENGES: CONDITION BASED AND PREDICTIVE MAINTENANCE

▪ Hardware and Infrastructure – Mobile asset, marit ime environment

▪ Useful data

▪ Quantity

▪ Interpretation

CHALLENGES: DATA INTERPRETATION

▪ Meaningful interpretation of data

▪ Idenitfying maintenance tasks

Expert Experience - Manual

Reliability Centred Maintenance - Manual

Diagnostic System – Automatic (can also be part of RCM)

GOALS?Improve availability and reduce overall maintenance cost

Improve maintenance scheduling speed and consistency

5HMAS WALLERSYDNEY HARBOUR, NSW

DIAGNOSTIC MAINTENANCE SCHEDULING▪ Diagnose machine health, risk of failure …▪ Schedule maintenance if and when required

System Interval A System Interval B System Interval C

PM Interval PM Interval PM Interval PM Interval PM Interval PM Interval

Schedule maintenance only when required

Interval A Interval B Interval C

DIAGNOSTIC MAINTENANCE SYSTEMFOR A COMMERCIAL OR NAVAL VESSEL COMPONENT

2. Maintenance Scheduling -Decision Theory

3. Performance Measurement -

Availability and Overall Maintenance Cost

1. Risk Assessment -Condition Monitoring and

Machine Learning

NUMBER 2 GENERAL SERVICE PUMP

COMPONENT APPLICATION FRAMEWORK

COMPONENT APPLICATION

VALUE = TRANSLATE + SCALE + FORECAST

Is it BETTER THAN periodic PM?

VALUE IN TRANSLATIONFOR COMMERCIAL OR NAVAL VESSEL APPLICATIONS

1. Create system at component level2. Tune and re-use for similar components on

same or different vessels

eg. Estimate system reduces maintenance cost of pump by 10% below current PM:

Per Pump : ~$80 AUD per year

Total for HTAs, 6 pumps : ~$500 AUD per year

Total RAN Fleet– 49 ships, boats, submarines, 10 pumps per vessel: ~$40,600 AUD per year

HTA ELWING HTA WAREE

Vessel

Sub-system 1

Component 1

Component 2

Sub-system 2

Vessel Vessel

VALUE IN SCALEFOR COMMERCIAL OR NAVAL VESSEL APPLICATIONS

1. Create systems at component level for

high priority components

2. Integrate systems to create higher

levels using RCM or alternatives

Add individual component savings

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0 5 10 15 20 25

Reliability of Component vs. Time

Corrosion Wear Fatigue

Each set of data points can be generated using system at t ime t, where R = 1 – F (mode(t)), also recommends an action and therefore maintenance cost

COMPLETED WORK TO MARCH 201810

DATA COLLECTION• Designed ten experiments, procured and installed hardware, completed experimental data collection and

processing• Designed CM data collection process, procured and installed hardware, completed 65% of data collection• Wrote scripts for data processing (experimental and CM)• Compiled equipment and maintenance data to date for Number 2 General Service Pump• Completed survey of Chief Engineer

METHODOLOGY• Identified novelty and strengths of methodology using literature review process• Developed new decision modelling theory in conjunction with supervisor (focus of second paper)• Designed and wrote scripts for methodology

WRITTEN COMMUNICATION OF RESEARCH• Literature review paper published in Ocean Engineering Journal• Internal Serco Hub article on research• Completed second paper draft – currently under review by supervisor• Drafted four chapters of Thesis

REMAINING WORK11

DATA COLLECTION• [September 2018] Complete remaining 2/3 of CM – 8 fortnightly sessions - 8 hours total time• Process remaining CM data• Record recommendations of Engineer and preventative maintenance alongside system

recommendations

METHODOLOGY• Tune model• Generate recommendations from CM data using tuned model• Graph recommendations from methodology, Engineer and PM schedule, calculate availability and

maintenance cost of the three policies

WRITTEN COMMUNICATION OF RESEARCH• Complete second paper draft and submission• Complete results paper draft and submission• Complete thesis

COMPONENT APPLICATION: NUMBER 2 GENERAL SERVICE PUMP

1. Risk Assessment - Condition Monitoring and Machine Learninga. Data for Algorithm Training and Condition Monitoring 13

b. Machine Learning Examples 23

c. Applying a Machine Learning Algorithm 24

2. Maintenance Scheduling - Decision Theorya. Maintenance Actions as Lotteries 25

b. Modelling Lottery Prizes: Multi-attribute Utility 26

c. Making a Decision: Maximum Expected Utility 27

3. Performance Measurement -Availability and Overall Maintenance CostAvailability and Maintenance Cost, Validation 28

DATA FOR MACHINE LEARNING AND CONDITION MONITORING

Two Purposes:

1. From Experiments on Test Pump- Build model

2. From Condition Monitoring on No. 2 General Service Pump – Use model to predict condition of No. 2 General service pump

CREATE DATASETS DESCRIBING COMMON CENTRIFUGAL PUMP FAULTS:

1. No fault – Run pump under normal operational conditions alongside2. No fault – Run pump under normal operational conditions engines running3. No fault – Run pump under normal operational conditions at sea

4. Worn Impeller - Lathe impeller fluid side and polish5. Worn bearing – Measure pump bearing with many running hours6. Damaged bearing – Grind outer race of new bearing flat and polish7. Unbalanced shaft/ Static Imbalance – Lathe off material from one point of shaft 8. Misaligned shaft/ Offset misalginment – Misalign pump- motor coupling 9. Loose packing – Loosen casing bolts10. Poor mounting – Loosen mounting bolt on pump foot

Two Purposes:

THE DATASETS (20 min sessions): SAMPLE RATE:

1. Vibration: Dual channel on pump Every 2 minutes

2. Temperature: Thermal imaging camera Per Minute

3. Pressure: Suction and discharge gauges Per Minute

4. Motor current: Current clamp on cord Per Minute

5. Packing drip rate: Visual inspection Per Minute

6. Shaft rotation: Tacometer Per experiment

ELWING BILGE/ FIRE SYSTEMTEMPORARY CONFIGURATION

Operating condit ions for all pumps:

-0.2 bar Suction

2.1 bar Discharge

TEST PUMP SETUP

NUMBER 2 GENERAL SERVICE PUMP

TEST RIG SETUP

DATA COLLECTION - EXPERIMENTAL

Two Purposes:

1. TEST PUMP/ AIR CONDITIONING PUMP – Conduct TEN EXPERIMENTS of 20 min sessions.

PhD Objective – Build a model which can detect the following:

1. No fault, no engines, ship alongside2. No fault, engines running, ship alongside3. No fault, ship at sea4. Worn Impeller5. Loose packing6. Damaged bearing7. Worn bearing (Air Conditioning Pump)8. Unbalanced shaft/Static Imbalance9. Misaligned shaft/Offset Misalignment10. Poor Mounting

Point 1 2 3 4 5 6 7 8 9 10

Measurement Vibration Vibration Vibration Temperature Temperature Temperature Vibration Temperature Temperature Vibration

LocationMotor, Vertical

Motor, Horizontal

Drive-end bearing

Pump CasingMotor, Drive End Bearing

CasingCoupling

Pump Drive End Bearing Casing,

HorizontalShaft

Pump, Bearing Casing

Pump Casing, Horizontal

Point 1 2 3 4 5 6 7 8 9 10

Motor, Horizontal

Drive-end bearing

CasingCoupling

HorizontalShaft

Two Purposes:

2. Number 2 General Service Pump – CONDITION MONITORING for one 20 min session, repeat fortnightly for 6 months.

PhD Objective - Detect the following using CM measurement:

1. No fault, alongside2. No fault, engines running3. No fault, at sea4. Worn Impeller5. Loose packing6. Damaged bearing7. Worn bearing8. Unbalanced shaft/Static Imbalance9. Misaligned shaft/Offset misalignment10. Loose mounting

Point 1 2 3 4 5 6 7 8 9 10

Motor, Horizontal

Drive-end bearing

CasingCoupling

HorizontalShaft

= MACHINE LEARNING CLASSIFICATION

APPLYING A MACHINE LEARNING ALGORITHM

▪ Results: Probability that pump is in each group:

OK - No faultImpeller wearDamaged PDE bearing …

▪ Naive Bayes AlgorithmSimple modelling approachGood performance on few data and many features

CM VectorMachine Learning

AlgorithmGroup

Probabilit ies

▪ Input: Set of Measurements from Number 2 General Service Pump:

VibrationTemperaturePressure …

MAINTENANCE ACTIONS AND HORSE RACING

MAXIMUM EXPECTED UTILITY

3. PERFORMANCE MEASUREMENT Availability vs. PM Overall Maintenance Cost vs. PM Validation against expert

recommendations

SUMMARY

Innovation needed in maintenance of commercial and naval vessels

Outlined a diagnostic maintenance system application to a shipboard pump

Tuning and validation of system is in progress (TBC September 2018)

29HMAS PERTHAUSTRALIAN MARINE COMPLEX COMMON USER FACILITY, WA, 2015

ACKNOWLEDGEMENTSThe candidate acknowledges the support of the ARC Research Training Centre for Naval Design and Manufacturing (RTCNDM) in this investigation Serco Defence Asia-Pacific and the Condition Monitoring division, Fleet Base East. The RTCNDM is a University- Industry partnership established under the Australian Research Council Industry Transformation grant scheme (ARC IC140100003). The candidate also acknowledges the support of Serco Defence Asia-Pacific and the Condition Monitoring Division, Fleet Base East in providing guidance and resources for this research.

THANKYOU! jane.cullum@utas.edu.au

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WORN IMPELLER

UNBALANCED SHAFT/ STATIC IMBALANCE

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DAMAGED BEARING

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MISALIGNED SHAFT/ OFFSET MISALIGNMENT

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VIBRATION DATA QUALITY

0 100 200 300 400 500 600 700 800 900 1000

Frequency Hz

Misaligned Shaft/OffsetMisalignmentNo fault alongside

Expect higher amplitudes at 25, 50 and 75Hz due to misaligned shaft - Mobius Institute Training Manual (2008)

0 100 200 300 400 500 600 700 800 900 1000

Frequency Hz

No fault alongside

Worn Impeller

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VIBRATION DATA QUALITY

520 Hz

Expect higher amplitudes at 25 and 520Hz due to worn impeller - Mobius Institute Training Manual (2008)

A DIAGNOSTIC MAINTENANCE SYSTEM - UNSW Canberra · 2018-05-11 · SYDNEY HARBOUR, NSW 5. ......

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