Annual Workshop Speaker: Olga Fink

Hybrid operational digital twins for complex systems: Fusing physics-based and deep learning algorithms for fault diagnostics and prognostics

Olga Fink, Professor of Intelligent Maintenance Systems, ETH Zürich


Deep learning algorithms need large amounts of representative data to learn relevant patterns. Although increasing amounts of condition monitoring data have been recently collected for complex systems, these data lack labels (in form of faults) and often also representativeness due to the high variability in operating conditions.  Integrating physics and structural inductive bias helps to overcome some of the limitations of deep learning algorithms. It reduces the amount of required training data, adds interpretability in the algorithms and makes some of the problems solvable that were not solvable before. Furthermore, it helps to build trust in the algorithms by making the outputs interpretable.

The talk will give some insights into operational digital twins developed by fusing physics-based and deep learning algorithms for fault diagnostics and prognostics.


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.

ETH Zürich website

Olga Fink at the ETH Zürich

Olga Fink on LinkedIn

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