作者：刘自然, 陈仁权, 颜丙生, 黄金来
作者单位：河南工业大学机电工程学院, 河南 郑州 450007
关键词：故障诊断;经验小波变换;包络谱分析;LabVIEW;轴承
摘要：
针对轴承故障早期信号非常微弱难以提取的特点，提出一种经验小波变换（EWT）和包络谱分析相结合的故障诊断方法。该方法应用EWT对信号进行自适应的分解处理，通过选取表征轴承故障的模态分量进行包络谱分析，对轴承故障进行判断，并在LabVIEW开发环境下实现，有效拓宽其适用环境。其中EWT是通过结合小波变换和经验模态分解各自的优点，建立自适应的小波滤波器来提取信号的模态函数。通过仿真信号和轴承故障实验信号的研究结果表明，LabVIEW开发环境下的EWT能够有效地对信号进行自适应分解，在与包络谱分析相结合后能够更为有效地提取并识别轴承故障类型。
Research on bearing fault diagnosis based on EWT and envelope spectrum analysis
LIU Ziran, CHEN Renquan, YAN Bingsheng, HUANG Jinlai
School of Mechanical and Electrical Engineering, He'nan University of Technology, Zhengzhou 450007, China
Abstract: In terms of the characteristics that early bearing failure signal is too weak to extract, a fault diagnosis method combining empirical wavelet transform(EWT) and envelope spectrum analysis is proposed. The method uses EWT to carry out self-adapting decomposition of the signal, and proceeds envelope spectrum analysis by selecting the mode components of representational bearing fault, so as to judge bearing faults, and it is programmed under the LabVIEW development environment, which effectively broadens its applicable environment. The EWT is an adaptive wavelet filter to extract the modal function of the signal by combining the advantages of wavelet transform and empirical mode decomposition. Based on the research on simulation signal and bearing failure test signal, the results show that the EWT under the LabVIEW development environment can effectively decompose the signal in self-adaption way and can more effectively extract and identify the bearing fault type after combining with the envelope spectrum analysis.
Keywords: fault diagnosis;EWT;envelope spectrum analysis;LabVIEW;bearing
2018, 44(2): 98-102 收稿日期: 2017-06-19;收到修改稿日期: 2017-08-15
基金项目: 国家自然科学基金（U1604134）
作者简介: 刘自然(1964-),男,河南信阳市人,教授,硕士,研究方向为动态测试技术、机电传动与控制技术。
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