Iterative hybrid causal model based diagnosis: Application to automotive embedded functions

Renaud Pons 1 Audine Subias 1 Louise Travé-Massuyès 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : This paper addresses off-line diagnosis of embedded functions, such as that made in workshops by the technicians. The diagnosis problem expresses as the determination of a proper sequence of tests and measures at available control points, which would lead to greedily localize the fault quickly and at the lowest cost. Whereas anticipated discrete faults can be properly addressed by fault dictionary methods based on simulation, a consistency based method designed for hybrid systems is proposed to address parametric faults and non-anticipated faults. This method uses those same inputs as the fault dictionary method and the only additional information is the structure of the reference models in the form of a causal graph and the interpretation of the simulation results into qualitative values and events. The consistency based diagnosis method is combined with a test selection procedure to produce an original iterative diagnosis method for hybrid systems that reduces diagnosis ambiguity at each iteration. The method is illustrated in the automotive domain with a real case study consisting in the electronic function commanding the rear windscreen wiper of a car.
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Renaud Pons, Audine Subias, Louise Travé-Massuyès. Iterative hybrid causal model based diagnosis: Application to automotive embedded functions. Engineering Applications of Artificial Intelligence, Elsevier, 2015, 37, pp.319-335. ⟨10.1016/j.engappai.2014.09.016⟩. ⟨hal-01400360⟩

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