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Prof. dr. ir. Tiedo Tinga
Life Cycle Management
T.Tinga@mindef.nl
Nederlandse Defensie AcademieNetherlands Defence Academy
PHM in context
Dynamics based Maintenance
T.Tinga@utwente.nl
utwente.nl/time
Netherlands Defence Academy
Ronde Tafel PHM
My background
• NLDA– Optimize maintenance and LCM– Focus on military systems (ships, helicopters, vehicles)
• University of Twente– Predictive Maintenance based on physical models– Structural Health and Condition Monitoring – Focus on civil applications (windturbines, bridges, train/track)– Part of Maintenance Consortium TIME
› Collaboration 8 research groups @UT› Combining multiple disciplines
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
Outline• Introduction
• Defining PHM
• Cases / applications
• Challenges
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
The Maintenance Challenge• Critical systems require preventive maintenance • Challenge: when to do maintenance ? • Balance between
– costs» spare parts, repair, man hours» long intervals
– reliability / availability» no unexpected failures» short intervals
• Optimal approach – on-condition maintenance (just-in-time)– both efficient (costs) and effective (no failures)
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
How to determine proper moment ?• Traditional approach: intervals based on
– estimate of future usage (OEM) often conservative
– collected failure data not always available (registration, PM)
– experience from the past not always representative
Experience-based and static
• Optimal approach– on-condition maintenance (just-in-time)– based on monitoring usage / loads / condition
Model-based and dynamic
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
Detection or Prediction of failures ?• Condition Monitoring / Anomaly detection
(diagnostics)– Monitor degradation of system with sensors or data
+ actual condition is accurately known+ based on measurements, independent of model assumptions-- mainly diagnosis immediate action required
• Prediction techniques (prognostics)– Focus on prediction of remaining useful life (RUL)
+ maintenance can be planned in future-- complex models or analyses required
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
Prognostics• Data- / experience-based approach
– Requires lots of (failure) data + some domain knowledge !– Data-mining
› Find correlations (represents past !)› Recognizes known failures (training)› Data analytics
• Model-based approach– Understand failure behaviour + loads– Also prognostics for changed operating profile– Monitor usage / loads / condition
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
Dynamic maintenance – model based
Failure model
Zoom in to the level of the physical failure mechanism
Usage Platform / system Remaining life
Local Loads Service life /Damage accumul.
thermal / fluid / structural model
Usage monitoring
Load monitoring Condition monitoring
Prognostics
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
• Prognostic distance
• Accuracy / reliability (false negatives / positives)
Prognostic performance
4-7-2017
Netherlands Defence Academy
PHM
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
What is PHM ?• Prognostics & Health Management (PHM)• Concept
– Optimize Life Cycle Management of system, using advancedsensors, models, algorithms
• Main constituents– Diagnosis
› asses present condition of asset / system– Prognosis
› predict remaining life time of asset / system– Health Management
› use diagnosis / prognosis to take proper decisions on maintenance activities
• Origin– USA - development of F35 / Joint Strike Fighter – Also applied in electronics / vehicles
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
sensors
data collection/registrations
data processing+ analytics
(physical) failure models
decision support
diagnosis
prognosis
health management
PHMCM / SHM
Pred.M.
Focus
RT
PHM elements
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
• Smart layer– 9 piezo actuators / sensors
• Optical fibres– Fiber Bragg gratings– Strain, temp., moisture
• ComparativeVacuum Monitoring
Sensor developments
4-7-2017
Netherlands Defence Academy
CASES
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
• HUMS system available for monitoring– Usage flight hours, landings, conditions, etc.– Health mainly vibrations
• Maintenance primarely related to flight hours
• Identified critical components (Pareto + CMMS)– Cost drivers– Availability killers
• Determined failure mechanism + governing loads
NH-90 helicopter prognostics
Heerink, 2013
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
• Landing gear shock absorber is critical• Time to failure not correlating to FH• Develop prognostic method
NH-90 helicopter prognostics
4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM
• Mechanism: wear of seal(oil leakage)
• Relevant Failure Parameter: travelled distance # landings + weight
NH-90 helicopter prognostics (2)
i iV k Fs=
4-7-2017
Netherlands Defence Academy
Optimization of maintenance
Mission type(incl. environment)
1. in harbour 2. training9. mission in
polar conditions
…….
Mission phase 1. transit 2. surveillance 12. anti-submarine…….
27% 17% 4%
10% 15% 15%
Subsystem usage
Gas turbine
2 (out of 4) diesel generators active
1 water chiller active
SMART-L
70%
0%
30%
2 water chillers active
3 water chillers active
70%
0%
0%
Mission type selection + mission duration (Tm)
• Determine optimal interval for subsystems• Depend on usage profiles• Integrate into ship level optimum
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
OPV system - installation - component
Diesel engine• Liner / ring
• Valves
• Bearing
• Many others …
Radar• PCB’s
• Bearing
• Many others …
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
Other applications• Predictive Maintenance
– Off-shore wind turbines drive train– Rail track / switch wear prediction– Production facilities – Electronics / PV modules
• Health & Condition monitoring– Bridges / sewer systems / wind turbine blades
• Decision support / LCM– Relating degradation to usage profiles– Selection of prognostics / CM system– Business case
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
CHALLENGES
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
PHM challenges• Relevant & high quality data is crucial
– How to select, collect and access ?• Developing and validating predictive models is
time-consuming – Not feasible for all systems how to prioritize ?– How can development be accelerated ?
• How to address gap component vs. system level ?• Also data-driven methods require system
knowledge + sufficient number of examples of anomalous behavior – How to generalize / automate ?– How to combine two approaches + CM ?
Ronde Tafel PHM 4-7-2017
Netherlands Defence Academy
Ronde Tafel PHM 4-7-2017
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