Towards a Data-driven Operational Digital Twin for Railway Wheels

Sustainable smart cities, transport systems and agriculture

Railway wheels are safety critical components and are a major cost driver for maintenance. Due to their criticality, they are tightly monitored by wayside monitoring systems. This opens the opportunity to develop algorithms for an end-to-end solution to a purely data-driven digital twin of railway wheels. However, challenges arise when dealing with real condition monitoring data. For example, the high variability of operating and environmental conditions impacting the captured signals. These can harm the performance of any data-driven model and ultimately, the digital twin. Furthermore, faults occur rarely in operating systems.

A training dataset typically does not cover any or only a subset of all possible fault types. Therefore, the implemented models need to be sensitive to changing health conditions in order to detect newly emerging fault types while being invariant to operating and environmental conditions. We propose contrastive learning to learn a feature representation that is on the one hand invariant to changing operating and environmental conditions while also being suited for the detection of novel fault types. We also discuss challenges of data collection and data labeling.

Olga Fink has been assistant professor of intelligent maintenance and operations systems at EPFL since March 2022.

Olga is also a research affiliate at Massachusetts Institute of Technology and Expert of the Innosuisse in the field of ICT. Olga’s research focuses on Hybrid Algorithms Fusing Physics-Based Models and Deep Learning Algorithms, Hybrid Operational Digital Twins, Transfer Learning, Self-Supervised Learning, Deep Reinforcement Learning and Multi-Agent Systems for Intelligent Maintenance and Operations of Infrastructure and Complex Assets. Before joining EPFL faculty, Olga was assistant professor of intelligent maintenance systems at ETH Zurich from 2018 to 2022, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). Between 2014 and 2018 she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW).

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 with Stadler Bussnang AG and as reliability and maintenance expert with Pöyry Switzerland Ltd. 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.