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Data-driven fault localization in cyber-physical systems using dependency graphs and anomaly detection
Citation Link: https://doi.org/10.15480/882.16136
Publikationstyp
Book Part
Date Issued
2025-03-17
Sprache
English
TORE-DOI
First published in
Number in series
399
Start Page
128
End Page
144
Citation
Frontiers in artificial intelligence and applications 399: 128-144 (2025)
Publisher DOI
Scopus ID
Publisher
IOS Press
ISBN
978-1-6436-8572-4
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
629.8: Control and Feedback Control Systems
Publication version
publishedVersion
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Name
FAIA-399-FAIA241577.pdf
Type
Main Article
Size
4.21 MB
Format
Adobe PDF