Study on Fault Diagnosis Method and Application of Automobile Power Supply Based on Fault Tree-Bayesian Network

Abstract

Among various fault types of automotive power faults, power supply UB + faults have the most complex relationship between fault signs and fault points and are difficult to diagnose. So this paper proposes a Bayesian network fault diagnosis model of automobile power supply based on fault tree. Firstly, based on the in-depth analysis of the principle of automobile power supply fault, the UB + fault tree model is constructed. The fuzzy Bayesian network model of UB + fault is constructed through the mapping relationship between fault tree and Bayesian network. Then, the prior probability of UB + fault points are obtained according to the five-year fault dataset of FAW Volkswagen Reck system, and the relevant conditional probabilities are determined by fuzzy set theory due to the lack of data and the uncertainty in expert scoring. Finally, the relevant fault point probability values are determined according to the Bayesian network inference algorithm in the case of single or parallel UB + fault sign occurring, and the fault diagnosis sequence is guided, further improving fault diagnosis efficiency.