基于经验模态分析的机床主轴轴承外圈非接触式故障检测方法  

Fault detection method of machine tool bearing based on empirical modal analysis

作　　者：刘斌 刘佳 张海鹏 LIU Bin;LIU Jia;ZHANG Haipeng(Henan Orchard Management Special Robot Engineering Center,Luoyang 471000,CHN;Luoyang Bearing Science&Technology Co.,Ltd.,Luoyang 471039,CHN)

机构地区：[1]河南省果园管理特种机器人工程技术研究中心,河南洛阳471000 [2]洛阳轴承科学技术研究所,河南洛阳471039

出　　处：《制造技术与机床》2023年第1期21-28,共8页Manufacturing Technology & Machine Tool

基　　金：精密机床主轴轴承工业性验证平台及性能评估体系(2018YFB2000505)。

摘　　要：针对主流机床的电机主轴轴承外圈故障检测问题,提出1种利用机床主轴电机定子电流信号进行非接触式故障诊断的方法,利用经验模态分解(EMD)对机床电机非平稳定子电流信号进行分析。采用经验模态分解方法提取定子电流信号的本征模函数(IMF)应用于维格纳分布(WVD),得到故障信号的维格纳分布轮廓图,最终利用人工神经网络进行故障样本的模式识别,可有效检测机床主轴轴承外圈缺陷。试验结果表明,在不同负载条件下,基于经验模态分解的维格纳分布定子电流监测对外圈缺陷的故障检测和诊断具有准确率高、计算量小以及检测成本低等优点,具有一定的工程实用及推广价值。In order to solve the problem of fault detection of the outer ring for the motor spindle bearing using in the mainstream machine, a non-contact fault diagnosis method using the stator current signal of the machine tool spindle motor was proposed. The empirical mode decomposition(EMD) was used to analyze the non-stationary stator current signal of the machine tool motor, and the eigenmode function(IMF) of the stator current signal was extracted by the empirical mode decomposition method and applied to the Wigner-Ville distribution(WVD) to obtain the fault signal. Finally, the artificial neural network was used for pattern recognition of fault samples, which can effectively detect defects in the outer ring of machine tool spindle bearings. The test results show that the stator current monitoring with Wigner distribution based on empirical mode decomposition has the advantages of high accuracy, small amount of calculation and low detection cost. It has certain engineering practical and popularization value.

关 键 词：机床轴承故障检测 经验模态分解 维格纳分布 前馈人工神经网络 

分 类 号：TH133.3[机械工程—机械制造及自动化]