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 diagnosis 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.
This paper deals with a benchmark-based experimental comparison of three diagnoser-based approaches for fault diagnosis of discrete event systems modeled by Petri nets: the MBRG/BRD approach, the FMG/FMSG approach and the SSD approach. The experiments are performed on a level crossing benchmark, using the respective software tools integrating the approaches. Different features are shown in terms of state-space building (exhaustive or partial), procedure for analyzing diagnosability (based on complete or on-the-fly built state-space) and state-space representation (concrete or symbolic). Based on the obtained experimental results, a comparative discussion is provided particularly regarding memory and time consumption for analyzing diagnosability of the three techniques.
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