基于BO优化SVM轴承故障的静电信号识别方法  

Fault electrostatic recognition for bearings via SVM optimizedby Bayesian optimization

作　　者：吴江平 刘若晨 孙见忠[2] 左洪福[2] 张兰春[1] Wu Jiangping;Liu Ruochen;Sun Jianzhong;Zuo Hongfu;Zhang Lanchun(School of Automobile and Traffic Engineering,Jiangsu University of Technology,Changzhou 213001,China;College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]江苏理工学院汽车与交通工程学院,常州213001 [2]南京航空航天大学民航学院,南京211106

出　　处：《电子测量技术》2024年第18期15-22,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(51705221,U2233204,52072176);江苏理工学院研究生实践创新计划项目(XSJCX23_71)资助。

摘　　要：针对新型静电监测技术应用于滚动轴承故障诊断时,静电信号易受干扰、故障识别率偏低的问题,提出了一种基于贝叶斯优化(BO)与支持向量机(SVM)相结合的滚动轴承故障静电信号识别方法。首先,通过搭建的静电仿真试验平台,采集轴承高速下不同磨损状态的静电信号,根据时域特征参数选取不同工况下的特征集;再对该模型最小误差的超参数进行选取,达到完成诊断模型训练的效果,用训练后混淆矩阵结果来评估各个模型的诊断精度。研究结果表明,本方法对静电监测下不同故障特征的轴承均具有一定识别能力,贝叶斯优化算法可以有效提高识别效率,其平均识别准确率可达98.82%。Aiming at the problem of easy interference of electrostatic signal and low fault recognition rate when the new electrostatic monitoring technology is applied to rolling bearing fault diagnosis,a method of electrostatic signal recognition of rolling bearing fault based on the combination of Bayesian optimization SVM is proposed.First of all,through the electrostatic simulation test platform constructed,the electrostatic signals of different wear states high speed are collected,and the feature sets of different working conditions are selected according to the time-domain feature parameters;and then the hyper-parameters of the minimum error of SVM are selected using Bayesian optimization to achieve the effect of completing the diagnostic model training,and the diagnostic accuracy of the models is evaluated with the results of the confusion matrix after training.The research results show that this method has certain recognition ability for bearings with different fault characteristics under electrostatic monitoring,and the Bayesian optimization algorithm can effectively improve the recognition efficiency,and its average recognition accuracy can reach 98.82%.

关 键 词：滚动轴承 故障识别 静电监测 支持向量机 贝叶斯优化 

分 类 号：TH133.33[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]