Applications of Bayesian Network in Fault Diagnosis of Braking System

Braking system is very important in automotive equipments. To solve the problems caused by the complex failure mechanisms in automotive hydraulic brake systems and the uncertain linkages between fault type and fault symptoms, a method of Bayesian network is offered for failure diagnosis. A statistical strategy is adopted there in this algorithm on the rule base provided by many experts to discard the weak causal relationship rules while keep the relatively strong ones during the Bayesian network structure learning, and a Bayesian network-based model of layered architecture is established for fault diagnosis of hydraulic braking systems. The experimental data shows that the diagnostic result obtained by using Bayesian network model is much more accurate than that by fuzzy logic method, thus the uncertainty existing in fault diagnosis is effectively solved.