• SNSF (Swiss National Science Foundation) Professor for intelligent maintenance systems at ETH Zürich • Before, she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW) • Holds Ph.D. in civil engineering from ETH Zurich, and Diploma degree in industrial engineering from Hamburg University of Technology • Gained valuable industrial experience as reliability engineer for railway rolling stock and as reliability and maintenance expert for railway systems • Research focuses on Data‐Driven Condition‐Based and Predictive Maintenance, amongst others Prof. Dr. Olga Fink, ETH Zurich 1
Transcript
• SNSF (Swiss National Science Foundation) Professor for intelligent
maintenance systems at ETH Zürich
• Before, she was heading the research group “Smart Maintenance” at
the Zurich University of Applied Sciences (ZHAW)
• Holds Ph.D. in civil engineering from ETH Zurich, and Diploma degree
in industrial engineering from Hamburg University of Technology
• Gained valuable industrial experience as reliability engineer for railway
rolling stock and as reliability and maintenance expert for railway
systems
• Research focuses on Data‐Driven Condition‐Based and Predictive
Maintenance, amongst others
Prof. Dr. Olga Fink, ETH Zurich
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Prof. Dr. Olga Fink
06.03.2019
Deep learning and artificial intelligence for predictive
maintenance applications
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Examples of what AI can do today
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Recognize ObjectsSpot cancer in tissue slides
Hold Interviews at Press Conferences
Teach Itself How to Code
Write film scriptsPaint paintings that
are sold
Source: www.businessinsider.com
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In which areas does AI excel today?
Artificial Narrow Intelligence
Artificial General Intelligence
ArtificialSuperintelligence
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Application Fields of Deep Learning and Artificial Intelligence
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Predictive
Maintenance
Computer Vision Natural Language Processing Speech Recognition
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Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
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Learning the “health” indicator
“Health Indicator”
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Learning
features
automatically
Michau, G., T. Palmé, and O. Fink (2017): Deep Feature Learning Network for Fault Detection and Isolation, Annual conference of the PHM society, October 2017
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Generator Health Monitoring
320 monitoring sensors:
Partial discharge
Rotor shaft voltage
Rotor flux
Stator end winding vibration
Stator Water Temperature
275 days of recorded operation,
60 000 observations
1 faultCan only use Healthy data for training!
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Michau, G., T. Palmé, and O. Fink (2017): Deep Feature Learning Network for Fault Detection and Isolation, Annual conference of the PHM society, October 2017
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Generator Health Monitoring
HealthyAbnormal behavior 100 days before!
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Time [days]
No
rmali
zed
dis
tan
ceto
the
train
ing
set
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Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
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06.03.2019Olga Fink
Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
Classification (that is typically applied in fault isolation) not suitable for distinguishing between the fault types
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Integrated fault diagnostics
“Health Indicator”
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Michau, G., T. Palmé, and O. Fink (2017): Deep Feature Learning Network for Fault Detection and Isolation, Annual conference of the PHM society, October 2017
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Water Temperature Shaft Voltage Rotor Flux Rotor Flux
Integrated fault diagnostics: Generator case study
At no additional cost!
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Michau, G., T. Palmé, and O. Fink (2017): Deep Feature Learning Network for Fault Detection and Isolation, Annual conference of the PHM society, October 2017
Integrated!
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Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
Classification (that is typically applied in fault isolation) not suitable for distinguishing between the fault types
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Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
Classification (that is typically applied in fault isolation) not suitable for distinguishing between the fault types
Varying and evolving operating conditions Even healthy
system conditions are not always representative
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Example Gas Turbines
Only a short observation period
Winter
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Example Gas Turbines
Olga Fink
Extend the
observation period
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Solution: Using the fleet experience! Transfer the experience
Transfer of experience with respect to the healthy
operating conditions Enlarge the set of
representative «healthy data»
Transfer the experience with respect to faulty system
conditions
But: single units are operated differently, have different configurations and
environmental conditions
Challenge: If fleet units too similar no additional experience added
If fleet units too different faulty system conditions recognized as healthy
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Using the fleet experience
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Michau, Gabriel, Thomas Palmé, and Olga Fink. 2018. “Fleet PHM for Critical Systems: Bi-Level Deep Learning Approach for Fault Detection.” In
European Prognognostics and Health Management Conference. Utrecht.
A
B
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A B
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Fleet of Gas Turbines Plant 1: Healthy
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Michau, Gabriel, Thomas Palmé, and Olga Fink. 2018. “Fleet PHM for Critical Systems: Bi-Level Deep Learning Approach for Fault Detection.” In
European Prognognostics and Health Management Conference. Utrecht.
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Fleet of Gas Turbines Plant 2: Fault
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Michau, Gabriel, Thomas Palmé, and Olga Fink. 2018. “Fleet PHM for Critical Systems: Bi-Level Deep Learning Approach for Fault Detection.” In
European Prognognostics and Health Management Conference. Utrecht.
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Challenges in Predictive Maintenance
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Few occurring faults with very specific characteristics + Large variety of different fault types Not sufficient examples for the algorithms to learn from
Classification (that is typically applied in fault isolation) not suitable for distinguishing between the fault types
Varying and evolving operating conditions Even healthy
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