Dr. Olga Fink, Data-Driven and hybrid algorithms for intelligent maintenance of complex industrial assets, 16:00-17:00, April 14 (Tuesday), 2020, Online Lotus Pond Rain Classroom: W141WB, ZOOM ID: 355 084 1457, Tencent ID: 147 561 5312020.04.12 204

[Time] 16:00-17:00, April 14 (Tuesday), 2020
 
[Location] Online
Lotus Pond Rain Classroom: W141WB
ZOOM ID:  355 084 1457
Tencent ID: 147 561 531
 
[Speaker]  Dr. Olga Fink

[Host] Dr. Yanfu Li
 
[Title] Data-Driven and hybrid algorithms for intelligent maintenance of complex industrial assets

Note: We will register the seminar for the attendees by the record in Lotus Pond Rain Classroom.

[Abstract] 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. However, faults in safety critical systems are rare. 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. Consequently, faulty conditions cannot be used to learn patterns from. Even collecting a representative dataset with all possible operating conditions can be a challenging task since the systems experience a high variability of operating conditions. Therefore, training samples captured over limited time periods may not be representative for the entire operating profile. The collection of a representative dataset may delay the implementation of data-driven fault detection and isolation systems.
The talk will give some insights into current challenges and highlight potential solutions for fault detection, isolation and the prediction of the remaining useful lifetime in the context of rare faults and a high variability of operating conditions. The talk will particularly present solutions in the field of domain adaption, transferring models between different units of a fleet and between different operating conditions, and hybrid approaches fusing physical performance models and deep neural networks.

[Bio] Olga Fink has been assistant professor of intelligent maintenance systems at ETH Zürich since October 2018. Olga is also a research affiliate at the Massachusetts Institute of Technology and an Expert of the Innosuisse, Swiss Innovation Agency, in the field of Information and Communication Technology. Olga’s research focuses on Intelligent Maintenance Systems, Data‐Driven Condition‐Based and Predictive Maintenance, Hybrid Approaches Fusing Physical Performance Models and Deep Learning Algorithms, Deep Learning and Decision Support Algorithms for Fault Detection and Diagnostics of Complex Industrial Assets. Before joining ETH faculty in 2018, she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW) between 2014 and 2018. Olga received her Ph.D. degree from ETH Zurich with the thesis on “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems”, and Diploma degree in industrial engineering from Hamburg University of Technology. She has gained valuable industrial experience as reliability engineer for railway rolling stock with Stadler Bussnang AG and as reliability and maintenance expert with P?yry Switzerland Ltd. Olga has received several research grants, including the highly competitive Professorship Grant of the Swiss National Science Foundation. Olga has been the chair on the Technical Committee Land Transportation of the European Safety and Reliability Association (ESRA) and is heading the Intelligent Maintenance Systems Network at ETH Zürich. In 2018, Olga was selected as one of the “Top 100 Women in Business, Switzerland” and in 2019, she was selected as young scientist of the World Economic Forum.

All interested are welcome!

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