Data-driven fault localization in cyber-physical systems using dependency graphs and anomaly detection

First published in
Frontiers in artificial intelligence and applications  
Number in series
399
Start Page
128
End Page
145
Citation
Frontiers in artificial intelligence and applications 399: 129-145 (2025)
Publisher DOI
10.3233/FAIA241577
Publisher
IOS Press
ISBN of container
978-1-64368-572-4
The early and automatic detection of faulty behavior is essential for maintaining the reliability of a cyber-physical system. In this paper we describe a fault localization approach for such a highly complex distributed system, the optical synchronization system of the European X-ray free-electron laser. Using a dependency graph, we model the relationships between the components and the influences of environmental effects. After we first resolve linear long-term dependencies between dependent components with a correlation analysis, we then use an unsupervised fault detection pipeline consisting of statistical feature extraction and unsupervised anomaly detection to accurately identify anomalies and localize their origins in the system.
Subjects
Data Mining
Fault Analysis
Dependency Graph
Cyber-Physical System
DDC Class
005.8: Computer Security
620: Engineering
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