基于改进EMD和双谱分析的电机轴承故障诊断实现  

Fault diagnosis of motor bearings using modified empirical mode decomposition and bi-spectrum

作　　者：陈宗祥 陈明星 焦民胜 葛芦生 CHEN Zong-xiang;CHEN Ming-xing;JIAO Min-sheng;GE Lu-sheng(College of Electrical and Information Engineering,Anhui University of Technology,Ma′anshan 243000,China)

机构地区：[1]安徽工业大学电气与信息工程学院,安徽马鞍山243000

出　　处：《电机与控制学报》2018年第5期78-83,共6页Electric Machines and Control

基　　金：国家自然科学基金(51277003)

摘　　要：轴承是电机设备极重要的部件。轴承故障检测是非常必要的。通过将改进的经验模态分解和双谱分析相结合的故障检测方法来有效诊断电机轴承的早期故障。首先,针对EMD分解无法得到严格单分量IMF的问题,利用小波包分解将轴承振动信号分解为窄带信号并选取能量最集中的频带进行重构,从而降低故障信号的复杂性,抑制模态混叠问题;然后利用经验模态分解方法根据信号的固有波动模式将其分解为一系列IMF分量;再通过方差贡献率检验去除其中的虚假分量;最后,利用双谱分析信号的调制关系进行解耦,得到故障特征频率。验证结果表明,所提出的分析方法能有效诊断轴承故障。Bearing plays an important role in the area of Motor.To ensure the safe and reliable operation of the motor,fault diagnosis of motor bearings is required.A fault feature extraction approach based on modified empirical mode decomposition and bi-spectrum was proposed to detect bearing incipient faults of motors in running condition.Firstly,the vibration signals were decomposed into individual frequency bands by wavelet packet and the highest energy band was reconstructed.Then EMD method was used to decompose the signal and gete a series of intrinsic mode function component,variance contribution was used to eliminate false components in EMD.Finally,the bi-spectrum was applied to identify these interactions and detect the bearing faults while it is still in an incipient stage.Through processing and analyzing the rolling bearing experimental data of West Reserve University,it shows the method is effective.

关 键 词：电机轴承 故障检测 改进经验模态分解 双谱 

分 类 号：H165[语言文字—汉语] TP206[自动化与计算机技术—检测技术与自动化装置]