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Title: Model based fault diagnosis system verification using reachability analysis
Authors: Su, Jinya
Chen, Wen-Hua
Keywords: Fault diagnosis
Fault estimation
Reachability analysis
Verification and validation
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: SU, J. and CHEN, W-H., 2017. Model based fault diagnosis system verification using reachability analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, In Press.
Abstract: In model-based Fault Detection and Isolation (FDI) systems, Fault Indicating Signals (FIS) such as residuals and fault estimates are corrupted by various noises, uncertainties and variations. It becomes challenging to verify whether a FDI system still works or not in real life applications. It is also challenging to select a threshold so that false alarm rate and missed detection rate are kept low depending on real operation conditions. This paper proposes solutions to the aforementioned problems by quantitatively analyzing the effect of uncertainties on FIS. The problems are formulated into reachability analysis problem for uncertain systems. The reachable sets of FIS are calculated under normal and selected faulty cases respectively. From these reachable sets, the effectiveness of a FDI system can be qualitatively verified under described uncertainties. A dedicated threshold can be further chosen to be robust to all possible described uncertainties. As a by-product, the minimum detectable fault can also be quantitatively determined by checking the intersection of the computed reachable sets. The proposed approach is demonstrated by evaluating a FDI algorithm of a motor in the presence of parameter uncertainties, unknown load and sensor noises, where a fault estimation based approach is adopted to diagnose amplifier, velocity and current sensor faults.
Description: This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
Sponsor: This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner
Version: Published version
DOI: 10.1109/TSMC.2017.2710132
URI: https://dspace.lboro.ac.uk/2134/25262
Publisher Link: https://doi.org/10.1109/TSMC.2017.2710132
ISSN: 2168-2216
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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