Fault Diagnosis Model of the Diesel Locomotive Air Brake System Based on Bayesian Network

Due to the configuration complexity of the diesel locomotive air brake system, it is difficult to realize the fault diagnosis on the brake system. In order to enhance fault diagnosis efficiency for diesel locomotive air brake system with uncertain fault, a fault diagnosis model based on Bayesian network is proposed in this paper. According to a priori exact probability or experts estimate that the probability, the classical Expectation-Maximization algorithm calculates the joint fault probability distribution and probability distribution of marginal respectively. Based on joint tree algorithm, Bayesian network is designed to infer the fault probabilities of components. The fault location could be realized. The simulation results indicate that the accurate fault probabilities could be calculated. Therefore, this method is effective for uncertain fault.