openHSU logo
Log In(current)
  1. Home
  2. Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg
  3. Publications
  4. 3 - Publication references (without full text)
  5. Modelling cyber-physical systems for fault diagnosis

Modelling cyber-physical systems for fault diagnosis

Publication date
2025-10-07
Document type
Forschungsartikel
Author
Diedrich, Alexander  
Krysander, Mattias
Heesch, René  
Niggemann, Oliver  
Organisational unit
Informatik im Maschinenbau  
DOI
10.1109/TSMC.2025.3614484
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/21329
Publisher
IEEE
Series or journal
IEEE Transactions on Systems, Man, and Cybernetics: Systems
ISSN
2168-2216
Periodical volume
55
Periodical issue
12
First page
9266
Last page
9279
Peer-reviewed
✅
Part of the university bibliography
✅
Funding(s)
Publikationsfonds der HSU/UniBw H  
Additional Information
Language
English
Keyword
Causality
Fault diagnosis
Operation modes
Satisfiability modulo theory
Abstract
Existing algorithms for consistency-based fault diagnosis are sound and complete according to some correct logical model. But obtaining a good model is the crucial and difficult part. Originally, the classical diagnosis algorithms were developed to analyze Boolean circuits, such that simple propositional or predicate logic models were sufficient to express the structure and behavior. Cyber–physical systems, however, exhibit hybrid behavior, meaning they generate continuous and discrete values that need to be interpreted. This adds significant complexity. Furthermore, modern cyber–physical systems may change their structure over their lifetime and thus require model adaptations. This article presents a novel formalism to model cyber–physical systems for consistency-based fault diagnosis. Drawing from the research fields of artificial intelligence and control theory, the approach models system structure and behavior through the use of satisfiability modulo nonlinear arithmetic. The approach proves advantageous compared to previous modeling techniques through its integration of nonlinear behavior models, implicit computation of residual values to compute fault symptoms, its representation of different operating modes of the system, and its integration with existing sound and complete fault diagnosis algorithms. The approach was validated empirically using two benchmarks from the process industry, a simulation of battery packs, and Boolean standard circuits. Throughout all experiments, an accuracy of 97% was achieved.
Description
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Version
Published version
Access right on openHSU
Metadata only access

  • Privacy policy
  • Send Feedback
  • Imprint