Failure prognostics in a particle filtering framework ...Norme ISO 13381-1:2004 : " estimation of...
Transcript of Failure prognostics in a particle filtering framework ...Norme ISO 13381-1:2004 : " estimation of...
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Failure prognostics in a particle filtering framework – Application to a PEMFC stack
Marine JouinRafael Gouriveau, Daniel Hissel, Noureddine Zerhouni, Marie-Cécile Péra
FEMTO-ST Institute, UMR CNRS 6174, BesançonFCLAB Research Federation, FR CNRS 3539, Belfort
R e s e a r c h
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Marine Jouin – Journées inter-GDRs– 12/06/2014 2R e s e a r c h
Motivations
– Fuel Cell : an alternative to traditional energies
Several application fields Transportation, µ-cogeneration, Portable devices powering, Aerospace.
No mobile parts = good reliability
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Marine Jouin – Journées inter-GDRs– 12/06/2014 2R e s e a r c h
Motivations
– Fuel Cell : an alternative to traditional energies
Several application fields Transportation, µ-cogeneration, Portable devices powering, Aerospace.
No mobile parts = good reliability
– Current limitations
Major limitation: lifespan still too short
Socio‐economic aspects Cost reduction of PEMFC system
Public acceptance
Technological boltsStable hydrogen supply with high purity
Hydrogen storage
Current Necessary Current Necessary8000 h ‐ transportation100 000 h ‐ stationary
PerformancesEfficency Durability
2000 – 3000 h35‐40%25‐30%
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Marine Jouin – Journées inter-GDRs– 12/06/2014 2R e s e a r c h
Motivations
– Fuel Cell : an alternative to traditional energies
Several application fields Transportation, µ-cogeneration, Portable devices powering, Aerospace.
No mobile parts = good reliability
– Current limitations
Major limitation: lifespan still too short
– Prognostics and Health Management (PHM) : a solution ?
Object : taking decision at the right time to optimize system use and avoid failures
Socio‐economic aspects Cost reduction of PEMFC system
Public acceptance
Technological boltsStable hydrogen supply with high purity
Hydrogen storage
Current Necessary Current Necessary8000 h ‐ transportation100 000 h ‐ stationary
PerformancesEfficency Durability
2000 – 3000 h35‐40%25‐30%
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Marine Jouin – Journées inter-GDRs– 12/06/2014 3R e s e a r c h
Failure prognostics in a particle filtering framework
1. Backgrounds
2. Feature extraction and aging modeling
3. Prognostics based on particle filters
4. Conclusion
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Marine Jouin – Journées inter-GDRs– 12/06/2014 4R e s e a r c h
Failure prognostics in a particle filtering framework
1. Backgrounds- Prognostics and Health Management- Prognostics: a key element- PHM of PEMFC- First work and its limitations
2. Feature extraction and aging modeling
3. Prognostics based on particle filters
4. Conclusion
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1. Backgrounds
– Prognostics and Health Management (PHM)
Signals transformations: extraction / selection / descriptors generation
Data coming from sensors or transducers
System state of health, comparison of descriptors on-line / expected, detection and location of failuresCause of failure, isolation et
identification of the component responsible of the failure
Prediction of the future states of the system, RUL estimates
Recommended actions to accomplish the mission (maintenance, command)
Human-machine interface
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Marine Jouin – Journées inter-GDRs– 12/06/2014 6R e s e a r c h
1. Backgrounds
– Prognostics: a key element
Pronostic ≈ RUL estimates (Remaining Useful Life) Norme ISO 13381-1:2004 : " estimation of time to failure and risk for one or more existing and future failure
modes"
Main objectives
Estimation of the Remaining Useful Life(RUL)
Estimation of the probability of failure of the system at a given date
Taking into account uncertainty is a major issue Uncertainty / system Uncertainty / its use Uncertainty / sensors Uncertainty / prognostic model defined
RUL
t fail.0 tc RUL
t fail.0 tc RUL
t fail.0 tc0 tc
prob
state
S1
S2
S3
0,5
0,3
0,2 prob
state
S1
S2
S3
0,5
0,3
0,2
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1. Backgrounds
– Prognostics: a key element
Different approaches
Prognostic
Hybrid approaches
Data driven approachesModel-based approaches
• Transformation of raw data into behavioral models (learning)
• No degradation model a priori
• Good ability to catch nonlinearities
• Require a huge amount of data
• Analytical models of nonlinear phenomena
• Need a small quantity of data
• High computational cost
• Default / system specific
• Models hard to develop
• Benefit from the advantages of both approaches
• Better model learning
• Better uncertainty management
• Can be complex and computationally expensive
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1. Backgrounds
– Prognostics: a key element
Prognostics objective: illustration
time
Degradation level
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1. Backgrounds
– Prognostics: a key element
Prognostics objective: illustration
time
Degradation level
Failure threshold
Critical threshold before failure
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1. Backgrounds
– Prognostics: a key element
Prognostics objective: illustration
Degradation level
Starting point of prediction : tp
Failure threshold
Critical threshold before failure
time
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1. Backgrounds
– Prognostics: a key element
Prognostics objective: illustration
time
Degradation level
End of lifeCritical threshold reached
Starting point of prediction : tp
RUL
RUL pdf
Failure threshold
Critical threshold before failure
Learning
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Marine Jouin – Journées inter-GDRs– 12/06/2014 8R e s e a r c h
1. Backgrounds
– Prognostics: a key element
Prognostics objective: illustration
time
Degradation level
End of lifeCritical threshold reached
Starting point of prediction : tp
RUL
RUL pdf
Failure threshold
Critical threshold before failure
time
RUL
tp1 tp2
RUL 1
RUL 2
Learning
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Marine Jouin – Journées inter-GDRs– 12/06/2014 9R e s e a r c h
1. Backgrounds
– PHM of PEMFC
M. Jouin, R. Gouriveau, D. Hissel, M-C. Péra, and N. Zerhouni, “Prognostics and health management of PEMFC state of the art and remaining challenges,” International Journal of Hydrogen Energy, vol. 38, no. 35, 15 307 – 15 317, 2013
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Marine Jouin – Journées inter-GDRs– 12/06/2014 10R e s e a r c h
1. Backgrounds
– PHM of PEMFC: challenges pointed out
Degradation, lossesand behavior
L1
L2
L3
L4
L5
L6
L7
Complex system
Data Acquisition
Data processing
Condition Assessment
Diagnostics
Decision Support
Human-Machine Interface
Observe
Model / Analyze
Decide Fault tolerant, self-adaptative and reconfigurable control system
Verification and validation procedures
Extended framework for detection and diagnostics approaches
Advanced prognostics models
Reliable, non-intrusive, non-damaging observation techniques
Easily implementable technology (cost, volume, online, etc.)
[1] M. Jouin, R. Gouriveau, D. Hissel, M-C. Péra, and N. Zerhouni, “Prognostics and health management of PEMFC state of the art and remaining challenges,” International Journal of Hydrogen Energy, vol. 38, no. 35, 15 307 – 15 317, 2013
Prognostics
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Marine Jouin – Journées inter-GDRs– 12/06/2014 10R e s e a r c h
1. Backgrounds
– PHM of PEMFC: challenges pointed out
Degradation, lossesand behavior
L1
L2
L3
L4
L5
L6
L7
Complex system
Data Acquisition
Data processing
Condition Assessment
Diagnostics
Prognostics
Decision Support
Human-Machine Interface
Observe
Model / Analyze
Decide Fault tolerant, self-adaptative and reconfigurable control system
Verification and validation procedures
Extended framework for detection and diagnostics approaches
Advanced prognostics models
Reliable, non-intrusive, non-damaging observation techniques
Easily implementable technology (cost, volume, online, etc.)
[1] M. Jouin, R. Gouriveau, D. Hissel, M-C. Péra, and N. Zerhouni, “Prognostics and health management of PEMFC state of the art and remaining challenges,” International Journal of Hydrogen Energy, vol. 38, no. 35, 15 307 – 15 317, 2013
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Marine Jouin – Journées inter-GDRs– 12/06/2014 11R e s e a r c h
1. Backgrounds
– First work and its limitations
Prognostics based on particle filters with simple empirical
aging models to predict the voltage degradation
3 models tested1. Linear2. Exponential3. Linear + logarithmic
Promising results with the 3rd but too much uncertainty on the results
Main limit = disturbances induced by characterizations
not taken into account
[2] M. Jouin, R. Gouriveau, D. Hissel, M-C. Péra, and N. Zerhouni, “Prognostics of PEM fuel cell in a particle filtering framework,” International Journal of Hydrogen Energy, vol. 39, no. 1, pp. 481 – 494, 2014
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Time in hours
RU
L
Linear model
Exponential model
Logarithmic model
Real RUL
90% confidence interval
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Marine Jouin – Journées inter-GDRs– 12/06/2014 11R e s e a r c h
1. Backgrounds
– First work and its limitations
Prognostics based on particle filters with simple empirical
aging models to predict the voltage degradation
3 models tested1. Linear2. Exponential3. Linear + logarithmic
Promising results with the 3rd but too much uncertainty on the results
Main limit = disturbances induced by characterizations
not taken into account
[2] M. Jouin, R. Gouriveau, D. Hissel, M-C. Péra, and N. Zerhouni, “Prognostics of PEM fuel cell in a particle filtering framework,” International Journal of Hydrogen Energy, vol. 39, no. 1, pp. 481 – 494, 2014
100 200 300 400 500 600 700 800 900-100
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Time in hours
RU
L
Linear model
Exponential model
Logarithmic model
Real RUL
90% confidence interval
PROBLEM ADDRESSED HERE
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Failure prognostics in a particle filtering framework
1. Backgrounds
2. Feature extraction and aging modeling- Principle- Modeling
3. Prognostics based on particle filters
4. Conclusion
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Marine Jouin – Journées inter-GDRs– 12/06/2014 13R e s e a r c h
2.Feature extraction and aging modeling
– Principle
1. Observation of the data
Different continuous aging parts separated by characterizations
Recovery observed after characterizations
Same trends of all the continuous aging parts but with an acceleration of the degradation
2. Selection of a model for continuous aging parts
Global model selected fromprevious work: P(t) = - a.ln(t) – b.t + c
Continuous aging parts
Characterizations
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2.Feature extraction and aging modeling
– Principle
3. Identification of coefficients a & b of the model on each part by robust least square fitting
0 20 40 60 80 100 120 140198
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0 10 20 30 40 50 60 70 80 90174
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Part 1: a1 & b1
Part 2: a2 & b2
Part n: an & bn
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2.Feature extraction and aging modeling
– Principle
4. Feature extraction from ai & bi i є [1, n]
5. Extraction of the recovery
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temps
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cup
fitted curveVrecup
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2.Feature extraction and aging modeling
– Modeling
Main objective: choosing models that are close to the data but can be justified by phenomena occurring within the stack
Global model for power aging: P(t) = - a.ln(t) – b.t + c
Models built thanks to feature extraction
for coefficient a aging: a(t) = a1.exp(a2.t) + a3.exp(a4.t)
for coefficient b aging: b(t) = b1.exp(b2.t) + b3
for recovery aging: R(t) = r1.exp(r2.t) + r3.exp(r4.t)
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Failure prognostics in a particle filtering framework
1. Backgrounds
2. Feature extraction and aging modeling
3. Prognostics based on particle filters- Data available- Development hypotheses- Problem formalization and adaptation- Particle filtering approach- Results
4. Conclusion
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Marine Jouin – Journées inter-GDRs– 12/06/2014 18R e s e a r c h
3. Prognostics based on particle filters
– Data available
data set: power degradation through time aging tests on a stack of 5 cells, 100cm²
FC : 1750h at constant current solicitation of 60 A
FCi
t = 1750h
0.6A/cm²
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3. Prognostics based on particle filters
– Development hypotheses
/ FC aging Degradation
– Irreversible with a long time constant– Not measurable directly (simply) deductible from another variable
Examples of possible candidates– Electrodes active surface area degradation – H2 crossover through the membrane
/ Functioning Constant current solicitation Constant operating conditions
/ Study framework Opening applicative limits: model
– Non-exact (unknown coefficients)– Non-stationary (time varying)– Non-linear– Non Gaussian noise
Aging observation through power evolution
Bayesian tracking
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Marine Jouin – Journées inter-GDRs– 12/06/2014 20R e s e a r c h
3. Prognostics based on particle filters
– Problem formalization
Formulation
Hidden state model Degradation state
Observation model Available measurements
Optimal Bayesian solution
Initial state distribution p(x0 | z0) ≡ p(x0) Obtaining of p(xk | z1:k) in 2 steps
1, ,k k k kx f x
,k k kz h x
1: 1 1 1 1: 1 1( / ) ( / ). ( / ).k k k k k k kp x z p x x p x z dx 1: 1
1:1: 1
( / ). ( / )( / )
( / )k k k k
k kk k
p z x p x zp x z
p z z
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Marine Jouin – Journées inter-GDRs– 12/06/2014 20R e s e a r c h
3. Prognostics based on particle filters
– Problem formalization
Formulation
Hidden state model Degradation state
Observation model Available measurements
Optimal Bayesian solution
Initial state distribution p(x0 | z0) ≡ p(x0) Obtaining of p(xk | z1:k) in 2 steps
1, ,k k k kx f x
,k k kz h x
1: 1 1 1 1: 1 1( / ) ( / ). ( / ).k k k k k k kp x z p x x p x z dx 1: 1
1:1: 1
( / ). ( / )( / )
( / )k k k k
k kk k
p z x p x zp x z
p z z
Problem adaptation
Modeling
Aging models developed earlier
Voltage and current measurements of the stack
Solving : particle filtering
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3. Prognostics based on particle filters
– Particle filtering approach
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Marine Jouin – Journées inter-GDRs– 12/06/2014 22R e s e a r c h
3. Prognostics based on particle filters
– Particle filtering approach
Principle
Filters associations to include characterizations
Filter 1: power aging P
Filter 2: coefficient a
Filter 3: coefficient b
Filter 4: recovery R
Feature extraction
Filters initialization
Prognostics by PF
LEARNING
Raw data
Behavior predictionRUL
PREDICTION
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3. Prognostics based on particle filters
– Particle filtering approach
Filters interactions
Threshold for learning or prognostics end not reached
Is a characterization scheduled ?
Update P with particles from models a, b & R
Filter 1
Yes
No
Filter 2
Filter 3
Filter4
P, a, b, R
t = t+1
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3. Prognostics based on particle filters
– Results
Behavior prediction (1/2)
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Prediction ended too early around 620 h
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3. Prognostics based on particle filters
– Results
Behavior prediction (2/2)
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Learning of 1300 hours
Good prediction
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3. Prognostics based on particle filters
– Results
Behavior prediction: discussion
MAPE during learning and prediction
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3. Prognostics based on particle filters
– Results
Behavior prediction: discussion
Feature extraction change with the length of the learning: illustration on recovery and one coefficient of the power model
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3. Prognostics based on particle filters
– Results
RUL estimates
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Marine Jouin – Journées inter-GDRs– 12/06/2014 29R e s e a r c h
Failure prognostics in a particle filtering framework
1. Backgrounds
2. Feature extraction and aging modeling
3. Prognostics based on particle filters
4. Conclusion
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Marine Jouin – Journées inter-GDRs– 12/06/2014 30R e s e a r c h
4. Conclusion
– Motivations Challenges
FC : technico-socio-economic stakes PHM : reliability / availability / costs – thematic of growing interest
Towards PHM of PEMFC : a lever to increase life duration
– Empirical modeling of aging Good way to represent power aging at constant current solicitation
Allows integrating recovery induced by characterizations
– Prognostics results Better prediction of power behavior
Less uncertainty in RUL estimates
But poor results if the learning is too short
– Planned expansion Take into account mission profiles / variable conditions by including the current in the models
Événement - date
Failure prognostics in a particle filtering framework – Application to a PEMFC stack
Marine JouinRafael Gouriveau, Daniel Hissel, Noureddine Zerhouni, Marie-Cécile Péra
FEMTO-ST Institute, UMR CNRS 6174, BesançonFCLAB Research Federation, FR CNRS 3539, Belfort
R e s e a r c h
ANR
PROPICE Summer School
Diagnostics and Prognostics of Fuel Cell Systems 01-04 July 2014, FCLAB, Belfort, France
https://propice.ens2m.fr/ecole-diag-pron-PAC.html Motivations and objectives
Fuel Cell Systems (FCS) appear to be a promising energy conversion device to face some of the economic and environmental challenges of modern society. However, even if this technology is close to being competitive, it is not yet ready to be considered for large scale industrial deployment: FCS still must be optimized, particularly by increasing their limited lifespan. Indeed, Proton Exchange Membrane Fuel Cell systems (PEMFC) usually have a life duration of around 2000 hours, whereas 6000 hours are required for some applications, including transportation... Enhancing FCS durability involves not only developing a better understanding of ageing phenomena but also requires the ability to emulate the behavior of the whole system to support the development of improvements to those systems. Prognostics and Health Management (PHM) of FCS is an emerging field of scientific and technological developments that has the potential to provide and enable improvements in the life management, use and support of Fuel Cell Systems. Objectives and program
The aim of this summer school is to provide a forum for researchers and practitioners to discuss PHM of Fuel Cell Systems, and identify actual and future research challenges in the area. Topics of “degradation mechanisms, diagnostic and prognostics of FCS”, as well as aspects related to the “social and economic challenges for a larger diffusion of FCS” will be addressed. Courses will combine:
� Academic and industrial lectures given by experts in the field; � Real case studies demonstrations with experimental manipulation on PEMFC platforms.
Program (see reverse side for more details) � Day 1: Introduction to Fuel Cell Technology � Day 2: Diagnostics and prognostics - backgrounds � Day 3: Socio-economic and industrial perspectives � Day 4: Case studies and demonstrations
Participants and registration
The school is open to both academics (from University) and professionals (from Industry). Scientists and practitioners interest in PHM technologies and application to Proton Exchange Membrane Fuel Cell (PEMFC) are encouraged to register. Registration fee (online registration, 200 €) includes:
� Summer School facilities; � Proceedings (hard copy); � Coffee breaks, daily lunches and gala dinner.