基于改进变分模态分解及循环相关熵谱的轴承故障诊断  

Bearing Fault Diagnosis Based on Improved Variational Mode Decomposition and Cyclic Correlation Entropy Spectrumon

作　　者：路鹏程 周凤星[1] 严保康[1] 陆翔宇 LU Peng-cheng;ZHOU Feng-xing;YAN Bao-kang;LU Xiang-yu(College of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,武汉430081

出　　处：《科学技术与工程》2023年第10期4210-4216,共7页Science Technology and Engineering

基　　金：国家自然科学基金(51975433)。

摘　　要：针对强背景噪声下非高斯脉冲噪声和高斯噪声对滚动轴承故障诊断产生严重干扰的问题,提出了一种基于改进变分模态分解(variational mode decomposition,VMD)并与循环相关熵谱(cyclic correntropy spectrum,CCES)相结合的故障诊断方法。首先,针对VMD传统重构指标易受噪声影响的问题,引入相关熵峭度(correlation entropy kurtosis index,CEK)指标对VMD分解后的模态分量进行选择与重构,去除高斯噪声;然后针对重构后信号仍存在的脉冲噪声影响问题,对重构信号进行CCES投影融合去除非高斯脉冲噪声干扰并增强特征;最后对融合结果进行分析与故障诊断。经仿真测试与实验表明,所提出的方法可以在高斯噪声和非高斯脉冲噪声背景下有效提取滚动轴承故障特征频率并实现故障诊断。A fault diagnosis method based on improved variational mode decomposition(VMD)combined with cyclic correntropy spectrum(CCES)was proposed to solve the problem that non-Gaussian impulse noise and Gaussian noise under strong background noise seriously interfere with the fault diagnosis of rolling bearings.Firstly,the correlation entropy kurtosis index(CEK)was introduced into the modal components after VMD decomposition for selection and reconstruction,so as to solve the problem that the traditional reconstruction index of VMD was susceptible to noise and remove Gaussian noise.Then,in view of the problem of impulse noise influence still existing in the signal after reconstruction,CCES projection fusion was proposed to reconstruct the signal into the non-Gaussian impulse noise interference and enhancement characteristics.Finally,the fusion results were analyzed and diagnosed.Simulation and experiment have shown that the proposed method can effectively extract the characteristic frequency of rolling bearing fault and realize fault diagnosis under gaussian noise and non-Gaussian pulse noise background.

关 键 词：循环相关熵谱 故障诊断 滚动轴承 变分模态分解 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP277[自动化与计算机技术—检测技术与自动化装置]