Adaptive Cruise System Wheel Speed Sensor Fault Diagnosis Based on IDBO-RBF Neural Network
If the adaptive cruise control system of a car malfunctions,it will cause huge economic losses in production and even directly endanger personal safety.This article proposes an adaptive cruise system wheel speed sensor fault diagnosis method based on the improved dung beetle optimizer(IDBO)optimized RBF neural network.Firstly,a fault simulation model for adaptive cruise control system wheel speed sensors is established using Matlab,and the charac-teristic parameters of the fault diagnosis model are extracted.Secondly,the dung beetle algorithm was optimized by integrating Levy flight and adaptive weights,and the optimized algorithm was used to establish an IDBO-RBF fault diagnosis model.Finally,compared with traditional BP and DBO-RBF models,the experimental results show that the IDBO-RBF model has a fault diagnosis accuracy of 96.8%,which can effectively diagnose the fault types of the adaptive cruise system wheel speed sensors.
adaptive cruise control systemdung beetle optimizer(DBO)RBF neural networkfault diagnosis
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