Set-membership functional diagnosability through linear functional independence

Abstract : Before a system is put into operation and eventually diagnosed, diagnosability analysis is an important stage. Diagnosability indeed guarantees that the sensored values delivered by the available instrumentation can be processed into an appropriate set of symptoms allowing to discriminate a reasonable set of faulty situations. A fault is considered as an additional parameter that impacts the behavior of some components of the system. Its effects may be linear or non-linear. Functional diagnosability, introduced in [1], and extended to set-membership (SM) functional diagnosability in [2] was analyzed through parameter identifiability. In the proposed work, SM-functional diagnosability is assessed from the linear independence of SM-functional fault signatures, which results in a much more direct test. [1] N. Verdière, C. Jauberthie, L. Travé-Massuyès, Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations, Journal of Process Control, Vol. 35, pp. 1-10, 2015. [2] C. Jauberthie, N. Verdière, L. Travé-Massuyès, Set-Membership diagnosability: definitions and analysis, In Proceedings of International Conference on Control and Fault-Tolerant Systems, 6 pages, Barcelona, Spain, 2016.
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Carine Jauberthie, Nathalie Verdière, Louise Travé-Massuyès. Set-membership functional diagnosability through linear functional independence. 10th Summer Workshop on Interval Methods, and 3rd International Symposium on Set Membership - Applications, Reliability and Theory, Jun 2017, Manchester, United Kingdom. 4p., 2017, 〈http://www.aerospace.manchester.ac.uk/our-research/ukim/news-and-events/swim-smart-2017/〉. 〈hal-01539647〉

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