Deep Learning for Fault Detection and Isolation...Prognostics and Health Management Data Acquisition...
Transcript of Deep Learning for Fault Detection and Isolation...Prognostics and Health Management Data Acquisition...
zhaw
Deep Learning for Fault Detection and Isolation
Hierarchical Networks for PHM
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 1
Gabriel Michau1, Thomas Palmé2, Olga Fink1
1 - Zurich University of Applied Science, Switzerland2 - General Electric (GE) Switzerland
Smart Maintenance Conference 2018
Zürich
zhaw
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 2
Prognostics and Health Management
Data Acquisition
Data Processing
Condition assessment(detection)
Diagnostics(identification)
Prognostics
Decision
• Features
• Raise early alarm
• Provide clear indication on the cause
Automatic process in a single approach
zhaw
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 3
Prognostics and Health Management
Data Acquisition
Data Processing
Condition assessment(detection)
Diagnostics(identification)
Prognostics
Decision
zhawChallenges of Critial Systems
PHM for critical (and complex) systems• Faults:
• Faults are rare • Faults cannot be afforded → preventive maintenance• Possible faults are numerous (possibly hitherto unknown)• Consequences of faults can be diverse
• System:• Heterogeneous data• Varied operating condition over long time scale• Unit Specificity
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 4
zhawHierarchical Neural Networks
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 5
“Health Indicator”
Trained with healthy condition monitoring data only
zhawTest in real case studies
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 6
Healthy
Abnormal behavior 100 days before!
Generator with inter-turn failure
zhaw
Water Temperature Shaft Voltage Rotor Flux Rotor Flux
In agreement with • other models (AAKR, PCA) • with expert knowledge.
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 7
At no additional cost!
Generator Diagnostics
zhawNew Challenges
What if, I don’t have enough training data?• What if my system is new (or has been refurbished)?• What if I am expecting operating conditions to change?
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 8
⇒Use data from other similar systems
Fleet approach to PHM (from manufacturer perspective)
zhawExample with a stator (vane failure)
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 9
Long training Short training
Winter
zhawHealthy Stator
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 10
Summer
Long training Short training
zhawSubfleet selection
• What data do I need?• Not everything at once because
1. It is too much2. It will contains operating points that are not relevant to my unit3. Completely different operating conditions might hide faults
⇒I need data relevant for my unit.
• Within a (manufacturer) fleet, all units are similar. ⇒Need to identify other units with similar operating conditions
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 11
zhawSubfleet selection
• Multi-dimensional datasets comparison• Resource and time consuming (multi-dimensional distance and distributions)• Difficult (number of nearest neighbors evolves as n.D)
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 12
zhawSubfleet selection
• Solution: The Hierarchical Network has proven to be efficient in measuring how well the test data correspond to the training data.
⇒Idea: 1. Train HELM on one unit, test with data from other units2. select those that are detected as similar.
⇒Computationally efficient: • HELM takes all dimensions and output a single indicator• 1 HELM to train per unit (instead of N2 dataset comparisons)
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 13
zhawProposed Methodology
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 14
HELMNew Stator “Healthy”
Training
1 – Train HELM on “new” stator
zhawProposed Methodology
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 15
TrainedHELM
New Stator
Test
Healthy Stator
1 – Train HELM on “new” stator2 – Test all other Stator
zhawProposed Methodology
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 16
HELMHealthy Stator “Healthy”
Training
1 – Train HELM on “new” stator2 – Test all other Stator
3 – Train HELM with other stator
zhawProposed Methodology
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 17
TrainedHELM
Healthy Stator
Test
New Stator
1 – Train HELM on “new” stator2 – Test all other Stator
3 – Train HELM with other stator4 – Test with “new” stator
zhawProposed Methodology
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 18
1 – Train HELM on “new” stator2 – Test all other Stator
3 – Train HELM with other stator4 – Test with “new” stator
5 – Dissimilarity measure:
Healthy StatorNew Stator
HELM
zhawSimilar datasets
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 19
Similar dataset if d below a threshold
Need to allow for more than 20% of dissimilar point to find onesimilar dataset for half of the dataset
This fleet has very dissimilar unitsDifficulty to find subfleets
zhawIndividual Asset Monitoring
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 20
HELMHealthy Stator
Subfleet “Healthy”
Training
New Stator
1 – Train HELM on “new” stator subfleet
zhawIndividual Asset Monitoring
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 21
trainedHELM
1 – Train HELM on “new” stator subfleet
2 – Monitor new data from individual asset
Test
New StatorNew data Health
Healthy StatorSubfleet
New Stator
zhawExamples
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 22
zhawExamples
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 23
zhawExamples
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 24
zhawExamples
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 25
zhawWhat is coming next?
• Fleet Feature analysis: What can be learned on the fleet?
• Transfer learning: What if my fleet have several versions of my system? (eg., not exactly the same data recorded)
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 26
zhaw
Thanks for your attention
04.09.2018 SMC 2018 – © ZHAW – Gabriel Michau 27
Acknowledgements