SUPERVISORS: Associate Professor Jonathan Binns, Professor Kiril Tenekedjiev, Dr. Rouzbeh Abbassi, Dr. Vikram Garaniya, Michael Lonsdale
A DIAGNOSTIC MAINTENANCE SYSTEMFOR COMMERICIAL AND NAVAL VESSELSJANE [email protected]
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
2
CHALLENGES: CONDITION BASED AND PREDICTIVE MAINTENANCE
▪ Hardware and Infrastructure – Mobile asset, marit ime environment
▪ Useful data
▪ Quantity
▪ Interpretation
3
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)
4
GOALS?Improve availability and reduce overall maintenance cost
Improve maintenance scheduling speed and consistency
5HMAS WALLERSYDNEY HARBOUR, NSW
5
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
PRED
ICTI
ONS
REQU
IREM
ENTS
6
DIAGNOSTIC MAINTENANCE SYSTEMFOR A COMMERCIAL OR NAVAL VESSEL COMPONENT
7
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
8
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
Fleet
Vessel
Sub-system 1
Component 1
Component 2
Sub-system 2
Vessel Vessel
VALUE IN SCALEFOR COMMERCIAL OR NAVAL VESSEL APPLICATIONS
9
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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VALU
E IN
FOR
ECAS
TIN
G
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25
Relia
bilit
y
Time
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
10
12
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
13
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
12
13
25
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
13
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
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
14
DATA FOR MACHINE LEARNING AND CONDITION MONITORING
ELWING BILGE/ FIRE SYSTEMTEMPORARY CONFIGURATION
Operating condit ions for all pumps:
-0.2 bar Suction
2.1 bar Discharge
15
16
TEST PUMP SETUP
NUMBER 2 GENERAL SERVICE PUMP
17
TEST RIG SETUP
DATA COLLECTION - EXPERIMENTAL
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
18
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
19
6
1
23
79
8
10
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
54
TEST
PUM
P
20
61
23
458
7
9
AIR
CON
DITI
ONIN
GPU
MP
10
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
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
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
21
DATA FOR MACHINE LEARNING AND CONDITION MONITORING
22
1
2 3
4 5 6
7
8 910
NUM
BER
2 GE
NER
AL
SERV
ICE
PUM
P
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
= MACHINE LEARNING CLASSIFICATION
23
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 …
24
MAINTENANCE ACTIONS AND HORSE RACING
25
MAXIMUM EXPECTED UTILITY
27
3. PERFORMANCE MEASUREMENT Availability vs. PM Overall Maintenance Cost vs. PM Validation against expert
recommendations
28
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! [email protected]
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WORN IMPELLER
UNBALANCED SHAFT/ STATIC IMBALANCE
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DAMAGED BEARING
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MISALIGNED SHAFT/ OFFSET MISALIGNMENT
41 of 18
VIBRATION DATA QUALITY
0
0.5
1
1.5
2
2.5
0 100 200 300 400 500 600 700 800 900 1000
Ampl
itude
mm
s-1
Frequency Hz
Misaligned Shaft/OffsetMisalignmentNo fault alongside
25 Hz
50 Hz
75 Hz
Expect higher amplitudes at 25, 50 and 75Hz due to misaligned shaft - Mobius Institute Training Manual (2008)
0
0.5
1
1.5
2
2.5
3
3.5
4
0 100 200 300 400 500 600 700 800 900 1000
Ampl
itude
mm
s-1
Frequency Hz
No fault alongside
Worn Impeller
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VIBRATION DATA QUALITY
25 Hz
520 Hz
Expect higher amplitudes at 25 and 520Hz due to worn impeller - Mobius Institute Training Manual (2008)
