A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN

Abstract

Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.