Research on the Prediction Model of Wheel Set Failure Rate for Urban Rail Trains Based on IPSO-BP Algorithm - HE Deqiang; SUN Yi; MENG Jiwei; LIU Jianren (Kongzhi Yu Xinxi Jishu)
In order to improve the prediction accuracy of wheel set failure rate of urban rail trains, the artificial neural network method was used to replace the empirical expression of failure rate distribution in the traditional maintenance strategy model to avoid the manaul selection of fault distribution models. The IPSO-BP (improved particle swarm optimization-back propagation) prediction model was established and compared with the conventional BP (back propagation) and PSO-BP (particle swarm optimization-back propagation) prediction model. Comparisons are made to verify its efficiency. The simulation results show that the prediction error range of IPSO-BP neural network model is 0~5.5%, and the relative error percentage of output value is 0~10%. The prediction accuracy of IPSO-BP neural network model is better than that of conventional methods, which can provide theoretical reference and methodological support for preventive maintenance decision-making.