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Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches

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Abstract

Real life experience has shown that intermittent faults are among the most challenging kinds of faults to detect and isolate, being present in the majority of production systems. Such a concern has made intermittent fault an active area of research in both discrete event and continuous-variable dynamic systems. In this paper, we present a review of the state-of-the art of intermittent fault diagnosability of discrete event systems modeled by finite state automata. To this end, we revisit the main definitions of diagnosability of intermittent faults, and present comparisons between them, consider verification and analysis techniques, and discuss available complexity results. Examples are used throughout the paper to illustrate the reviewed concepts and verification algorithms. We also look ahead, by suggesting some perspectives for future research.

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Notes

  1. As shown in Santoro et al. (2017), the case of multiple intermittent faults (and consequently recoveries) can be addressed by considering each fault type separately while assuming the other fault types as ordinary unobservable events.

  2. The only exception is the verifier proposed by Moreira et al. (2011). See also Kumar and Takai (2014) and Moreira et al. (2016).

  3. Every made assumption is to be applied to the remainder of the text unless explicitly indicated. Nevertheless, for the sake of clarity, we will indicate in all results which assumptions are being required.

  4. If the automaton that recognizes some language is non-deterministic, then it can be made deterministic by computing its observer (Cassandras and Lafortune 2008, p. 89).

  5. Some assumptions can be relaxed for some approaches. When this is the case, it will be indicated explicitly.

  6. Given a cycle cl in the diagnoser, we say that two cycles cl1 and cl2 in the system model are consistent with cl if event-sequences s1 and s2 associated with cl1 and cl2 respectively, and the event-sequence s associated with cl, share the same observation, i.e., P(s1) = P(s2) = scl.

  7. The diagnoser computation is presented at the beginning of this section (cf. Section 4.2.1).

  8. Differently from diagnosability, discriminability is the possibility to detect the exclusive occurrence of a particular behavior of interest.

  9. Prediagnosability consists in detecting the occurrence of an indicator trace which ensures that the fault occurrence is inevitable.

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Authors and Affiliations

  1. Institut de Recherche Technologique Railenium, F-59300, Famars, France

    Abderraouf Boussif

  2. COSYS-ESTAS, Univ Gustave Eiffel, IFSTTAR, Univ Lille, F-59650, Villeneuve d’Ascq, France

    Mohamed Ghazel

  3. Department of Electrical Engineering, Universidade Federal do Rio de Janeiro, 21949-900, Rio de Janeiro, Brazil

    João Carlos Basilio

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  1. Abderraouf Boussif
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The work of M. Ghazel was supported by ELSAT2020 project. ELSAT2020 is co-financed by the European Union with the European Regional development Fund, the French state and the Hauts de France Region Council.

The work of J. C. Basilio was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Finance Code 001, and the Brazilian Research Council (CNPq), grant number 309652/2017-0.

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Boussif, A., Ghazel, M. & Basilio, J.C. Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches. Discrete Event Dyn Syst 31, 59–102 (2021). https://doi.org/10.1007/s10626-020-00324-y

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