基于频域特征和支持向量机的汽车水泵轴承故障诊断研究  

Research on Fault Diagnosis of Automotive Water Pump Bearing Based on Frequency Domain Features and SVM

作　　者：唐静[1] 王二化[1] 朱俊[1] 谭文胜 TANG Jing;WANG Erhua;ZHU Jun;TAN Wensheng(Changzhou College of Information Technology,Changzhou Jiangsu 213164,China;Changzhou Key Laboratory of Large Plastic Parts Intelligence Manufacturing,Changzhou Jiangsu 213164,China)

机构地区：[1]常州信息职业技术学院,江苏常州213164 [2]常州市大型塑料件智能化制造重点实验室,江苏常州213164

出　　处：《机床与液压》2018年第13期163-167,155,共6页Machine Tool & Hydraulics

基　　金：江苏省高校自然科学研究面上项目(16KJD460008);常州市科技计划项目(CM20153001)

摘　　要：为了在线监测与识别汽车水泵轴承的故障类型,以WR3258152型汽车水泵轴承为研究对象,分析了其内部结构和常见故障。根据常见故障,预设了汽车水泵轴承的4类缺陷。在搭建的信号采集实验平台上,利用加速度传感器,分别采集了4类缺陷轴承在运转过程中的振动信号。利用Matlab软件对振动信号进行快速傅立叶变换和频域特征值计算,选用径向基核函数和粒子群参数优化方法建立支持向量机模型,并进行测试验证,结果表明:支持向量机分类方法能精确识别汽车水泵轴承常见的4类缺陷。为汽车水泵轴承的在线监测与故障诊断提供了参考。In order to monitor and identify the fault type of the automobile pump bearing on line,the internal structure and common faults of WR3258152 automobile water pump bearing as a research object are analyzed. According to the common faults,four kinds of defects of automobile water pump bearings were preset. On the established signal acquisition experimental platform,using acceleration sensor,the vibration signal of four kinds of defect bearings in operation were collected. Using MATLAB software for fast Fourier transform and frequency domain eigenvalue calculation of vibration signal,the Radial basis function（ RBF） and particle swarm optimization（ PSO） method were used to establish support vector machine（ SVM） model,and tested and verified. The results show that the SVM classification can identify four kinds of common defects of automobile pump bearings accurately. It provides reference for on line monitoring and fault diagnosis of automobile water pump bearing.

关 键 词：水泵轴承 频域分析 支持向量机 故障诊断 

分 类 号：TH133[机械工程—机械制造及自动化]