Learning system descriptions for cyber-physical systems
Publication date
2024-08-16
Document type
Konferenzbeitrag
Organisational unit
Conference
12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2024 ; Ferrara, Italy ; June 4–7, 2024
Publisher
Elsevier Science
Series or journal
IFAC-PapersOnLine
ISSN
Periodical volume
58
Periodical issue
4
First page
628
Last page
633
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Fault diagnosis algorithms compute faulty components by comparing actual observations against some model of known behaviour. A major challenge for fault diagnosis lies in creating such a suitable model. In the past, models were usually assumed to be given by experts. But in modern cyber-physical systems this assumption cannot be held, as experts are expensive and system architectures may be subject to change. This article presents a novel algorithm to obtain those models automatically and apply them for fault diagnosis. The evaluation was done on the Tennessee Eastman process and on two benchmarks of multiple-tank systems.
Description
This is an open access article under the CC-BY-NC-NDLicense (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Version
Published version
Access right on openHSU
Metadata only access