参数自适应TVF-EMD的滚动轴承故障诊断  

Parameter Adaptive Time Varying Filtering Based on Empirical Mode Decomposition with Application to Bearing Fault Diagnosis

作　　者：孙冬宁 曾小飞 莫贞凌 SUN Dong-ning;ZENG Xiao-fei;MO Zhen-ling(School of Aeronautics and Astronautics,Sichuan University,Sichuan Chengdu610065,China)

机构地区：[1]四川大学空天科学与工程学院,四川成都610065

出　　处：《机械设计与制造》2020年第4期171-176,共6页Machinery Design & Manufacture

基　　金：国家自然科学基金资助项目(51675355考虑多故障耦合的行星轮系振动响应特性及故障行为表征)。

摘　　要：针对目前出现的时变滤波经验模式分解(TVF-EMD)在滚动轴承故障特征提取中参数选择难的问题,提出了一种参数自适应TVF-EMD方法。首先利用粒子群优化(PSO)算法对TVF-EMD影响最大的参数组合进行优化,获得最佳的参数组合,并对故障信号进行TVF-EMD分解。然后筛选经分解故障信号获得的敏感固有模态函数(IMF),并进行包络解调运算。最后根据包络谱判断滚动轴承的故障。分别利用TVF-EMD、集合经验模式分解(EEMD)和变分模态分解(VMD)方法对仿真信号进行分析,表明TVF-EMD方法具有更优越的分解性能。利用参数自适应TVF-EMD方法对滚动轴承故障信号进行分析,表明参数自适应TVF-EMD方法可有效识别滚动轴承故障。To solve the problem of parameter selection in the process of rolling bearing fault feature extraction based on time varying filtering based empirical mode decomposition(TVF-EMD)technique,a parameter adaptive TVF-EMDis proposed in this paper.Firstly,Particle Swarm Optimization(PSO)was used to optimize the decomposition parameters of TVF-EMD so as to obtain the optimal parameter combination,and the fault signal was decomposed by TVF-EMD.Then,the sensitive intrinsic mode function obtained by decomposed fault signal was selected and the envelope demodulation operation was used to judge the fault type of rolling bearing according to the envelope spectrum.The simulated signal was analyzed by using TVF-EMD,Ensemble Empirical Mode Decomposition(EEMD)and Variational Mode Decomposition(VMD),which shows that the TVF-EMD method has superior decomposition performance.Using the parameter adaptive TVF-EMD method to analyze the fault signal of rolling bearing,it shows that the proposed method can effectively distinguish the fault of rolling bearing.

关 键 词：振动信号分析 故障诊断 模态混叠 TVF-EMD PSO算法 包络熵 

分 类 号：TH16[机械工程—机械制造及自动化] TH165.3