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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


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