基于IDBO-RBF的自适应巡航系统轮速传感器故障诊断  

Adaptive Cruise System Wheel Speed Sensor Fault Diagnosis Based on IDBO-RBF Neural Network

作　　者：谢春丽 毛海锋 XIE Chunli;MAO Haifeng(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学机电工程学院,哈尔滨150040

出　　处：《自动化与仪表》2024年第7期51-56,共6页Automation & Instrumentation

基　　金：黑龙江省自然科学基金项目(LH2021F002)。

摘　　要：汽车自适应巡航系统发生故障,将会造成巨大的生产经济损失,甚至直接危害人身安全。该文提出一种基于改进蜣螂算法(improved dung beetle optimizer,IDBO)优化RBF神经网络的自适应巡航系统轮速传感器故障诊断方法。首先,利用Matlab建立自适应巡航系统轮速传感器故障仿真模型,提取出故障诊断模型的特征参数;其次,用融合了Levy飞行和自适应权重对蜣螂算法进行优化,用优化后的算法建立IDBO-RBF故障诊断模型;最后,与传统BP和DBO-RBF模型进行对比验证,实验结果表明,IDBO-RBF模型在故障诊断精度上达到96.8%,可以有效诊断自适应巡航系统轮速传感器的故障类型。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 characteristic 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.

关 键 词：自适应巡航系统 蜣螂算法 RBF神经网络 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]