Title: Towards intelligent operation and maintenance of complex systems
Speaker: Dr. Olga Fink (ETH Zürich)|
Time: January 17, 20:00-20:55
Online meeting venue: Tencent meeting (Room ID: 718 753 799)
The amount of measured and collected condition monitoring data for complex infrastructure and 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. Moreover, some of the current limitations include the limited scalability, generalization ability and interpretability of the developed models. The talk will give an overview of the currently ongoing research at the chair of Intelligent Maintenance Systems at ETH Zürich, including 1) research in the field of domain adaptation and unsupervised transfer learning for fault detection and diagnostics at fleet level, 2) research on algorithms combining deep learning and physics-based approaches; 3) research in prescriptive operations and 4) research on multi-agent systems for decision support and maintenance scheduling.
Announced by the Shien-Ming Wu School of Intelligent Engineering