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Fault detection and diagnosis using Bayesian-network inference

This paper aims to develop a novel fault detection and diagnosis (FDD) method based on Bayesian network (BN). Firstly, the structure of BN is constructed according to the fault feature, which can be obtained from K2 algorithm and expertise. Then, corresponding parameters of BN are derived from dataset and expertise. Secondly, junction tree algorithm reasoning is provided. Finally, a framework of BN for vehicle start system fault diagnosis is proposed. There are three cases to verify the method. The maximum probability fault can be found according to the way of BN reference results, which demonstrates the feasibility of the developed method.