| Issue |
Security and Safety
Volume 1, 2022
|
|
|---|---|---|
| Article Number | 2022004 | |
| Number of page(s) | 29 | |
| Section | Industrial Control | |
| DOI | https://doi.org/10.1051/sands/2022004 | |
| Published online | 08 August 2022 | |
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A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security
Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Bismarckstr. 81 BB, 47057 Duisburg, Germany
* Corresponding author (email: steven.ding@uni-due.de)
Received:
8
February
2022
Revised:
7
March
2022
Accepted:
14
March
2022
This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems. It calls for more research attention on three aspects: (i) application of control and detection unified framework to enhancing the diagnosis capability of feedback control systems, (ii) projection-based fault detection, and complementary and explainable applications of projection- and machine learning-based techniques, and (iii) system performance degradation detection that is of elemental importance for today’s automatic control systems. Some ideas and conceptual schemes are presented and illustrated by means of examples, serving as convincing arguments for research efforts in these aspects. They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.
Key words: Diagnosis in automatic control systems / Cyber security in industrial cyber physical systems / Unified framework of control and detection / Projection-based diagnosis / Explainable application of ML-methods / Performance degradation detection
Citation: Ding SX. A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security. Security and Safety 2022; 1: 2022004. https://doi.org/10.1051/sands/2022004
© The Author(s) 2022. Published by EDP Sciences and China Science Publishing & Media Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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