- This event has passed.
Sep 20, 2018: Henry L. Pierce Laboratory Seminar Series – Prof. Olga Fink
September 20, 2018 @ 4:00 pm
Deep Learning for Fault Detection and Isolation in Complex Industrial Assets
The amount of measured and collected condition monitoring data for complex
industrial assets has been recently increasing significantly due to falling costs,
improved technology, and increased reliability of sensors and data transmission.
The measured condition monitoring signals of complex industrial assets are
typically high dimensional, highly redundant, have several interdependencies
and prevalent non-linear relationships. The diversity of the fault types and
operating conditions makes it often impossible to extract and learn the fault
patterns of all the possible fault types affecting a system. Even collecting a
representative dataset with all possible operating conditions can be a
challenging task (depending on the variability of the operating regimes of the
assets) and may delay the implementation of data-driven fault detection
The talk will elaborate how deep learning algorithms with an end-to-end learning
architecture for integrated automatic feature learning make it possible to
overcome some of the challenges in fault detection and isolation. The focus will
be on the recently proposed framework that combines an unsupervised feature
learning with a one-class classifier with the ability to learn features from the
healthy system conditions in an unsupervised way. Subsequently, these features
are used to distinguish between healthy and faulty system conditions. A further
focus will be on the selection of a representative training dataset covering
operating conditions that have not been experienced by a single asset but
originate from a fleet of similar assets.
Olga Fink is SNSF (Swiss National Science Foundation) professor for intelligent
maintenance systems at ETH Zürich (starting from October 2018). Before joining
ETH faculty, she was heading the research group “Smart Maintenance” at the
Zurich University of Applied Sciences (ZHAW).
Dr. Fink received her Ph.D. degree in Civil Engineering from ETH Zurich, and
Diploma degree in industrial engineering from Hamburg University of
Technology. She has gained valuable industrial experience as reliability engineer
for railway rolling stock and as reliability and maintenance expert for railway