Bus Fault Prediction Based on Bayesian Network

Based on three years of maintenance data of 56 buses with three types of fuel, the fault situation of buses has been analyzed. By using the regression method of least absolute shrinkage and selection operator (LASSO), the main influencing factors of bus fault risk were detected by establishing a LASSO model. Five machine learning methods were used to construct the bus fault risk prediction model, and the prediction accuracy was compared and analyzed. According to the prediction model results and corresponding analysis, the bus fault risk prediction system is constructed. Results show that 13 variables with the most significant correlation with bus fault are screened out by LASSO regression model. It is found that the Bayesian network model has the highest prediction accuracy of 86%. Furthermore, through this model analysis, the influences of various influencing factors on the occurrence of bus faults can be obtained more intuitively.