基于HHT边际谱熵-马氏距离的滚动轴承故障诊断  

Fault diagnosis of rolling bearing based on HHT marginal spectrum entropy-Mahalanobis distance

作　　者：李巧艺[1] 单奇[1] 陈跃威 

机构地区：[1]西南交通大学机械工程学院,四川成都610031

出　　处：《燕山大学学报》2016年第6期493-498,共6页Journal of Yanshan University

基　　金：国家自然科学基金资助项目(51275426;51475386)

摘　　要：针对滚动轴承振动信号的非平稳特性,依据HHT(Hilbert Huang Transform)理论和非线性熵概念,提出了一种基于HHT边际谱熵和马氏距离相结合的轴承故障诊断方法。首先对采集到的正常及故障轴承振动信号作小波变换阈值去噪,然后通过HHT得到Hilbert边际谱。依据广义信息熵的概念定义了边际谱能量熵函数,基于所提取的特征函数,采用马氏距离对轴承故障类型进行分类。实验结果表明,该方法可以准确、有效地实现轴承的故障判别,为实际滚动轴承故障诊断提供一定的理论参考。In the light of the non-stationary characteristics of the rolling bearing vibrating signals,a new fault diagnosis method is proposed for the rolling bearing based on HHT marginal spectrum entropy and Mahalanobis distance according to the Hilbert Huang transform theory and the concept of generalized information entropy. First,the known normal signals and fault signals measured in the same load but with different faults are pretreated based on the effective wavelet threshold de-noising. Second,the EMD,Hilbert spectrum and Hilbert marginal spectrum are analyzed by utilizing Hilbert-Huang transform technique, and the marginal spectrum energy entropy function is defined according to the concept of generalized information entropy. Finally,the Mahalanobis distance is used to classify the working state and fault type of the rolling bearing based on the feature function. The results show that the fault diagnosis of rolling bearing can be realized accurately and effectively by utilizing the proposed method. The proposed method can provide a good reference for the actual rolling bearing fault diagnosis.

关 键 词：HHT 边际谱熵 马氏距离 故障诊断 

分 类 号：TH18[机械工程—机械制造及自动化]