Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.

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Abstract-In this paper, we are interested in the diagnosis of discrete event systems modeled by finite transition systems. We propose a model of supervision patterns general enough to capture past occurrences of particular trajectories of the system. Modeling the diagnosis objective by a supervision pattern allows us to generalize the properties to be diagnosed and to render them independent of the description of the system. We first formally define the diagnosis problem in this context. We then derive techniques for the construction of a diagnoser and for the verification of the diagnosticability based on standard operations on transition systems. We show that these techniques are general enough to express and solve in a unified way a broad class of diagnosis problems found in the literature, e.g. diagnosing permanent faults, multiple faults, fault sequences and some problems of intermittent faults.

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