基于DIA的最优尺度形态滤波器及其在轴承故障诊断中应用  

Optimal Scale Morphological Filter Based on DIA and Its Application in Bearing Fault Diagnosis

作　　者：杨滨 和丹 祝丽莉 张丽洁 魏豪 YANG Bin;HE Dan;ZHU Lili;ZHANG Lijie;WEI Hao(School of Mechanical and Electrical Engineering,Xi′an Polytechnic University,Xi′an 710048,China;Suzhou Veizu Equipment Diagnostic Technology Co.,Ltd.,Suzhou 215200,Jiangsu,China)

机构地区：[1]西安工程大学机电工程学院,西安710048 [2]苏州微著设备诊断技术有限公司,江苏苏州215200

出　　处：《噪声与振动控制》2023年第5期181-187,共7页Noise and Vibration Control

基　　金：陕西省自然科学基础研究计划资助项目(2022JM-362);大学生创新创业训练资助项目(202110709007)。

摘　　要：针对强背景噪声下无法获取滚动轴承故障特征的问题,提出一种基于最优尺度增强形态滤波器(Optimal Scale Enhanced Morphological Filter,OEMF)的滚动轴承故障特征提取方法。首先,构建一种增强差分积形态滤波算子(Enhanced Different Product Operation,EDPO)与直线型结构元素(Structuring Element,SE)相结合的增强形态滤波器;其次,构建冲击自相关度(Degree of Impact Autocorrelation,DIA)作为选取最优尺度的评判指标,并与传统的峭度(Kurtosis)指标进行比较。最后,将所提方法应用于仿真信号和牵引电机轴承故障信号中进行实验验证。结果表明,所提方法与集成经验模式分解(Ensemble Empirical Mode Decomposition,EEMD)方法相比具有更好的降噪效果,且在去除噪声干扰方面具有更好鲁棒性。In view of the problem that the fault features of rolling bearings cannot be obtained by intense background noise,a fault diagnosis method of rolling bearings based on optimal scale enhanced morphological filter(OEMF)is proposed.Firstly,an enhanced morphological filter is constructed by combining the enhanced different product operation(EDPO)with the flat structuring element(SE).Then,the degree of impact autocorrelation(DIA)is constructed as the evaluation index to select the optimal scale,and compared with the traditional kurtosis index.Finally,the proposed method is applied to the simulation signals and traction motor bearing fault signals for experimental verification.The results show that the proposed method has better noise reduction effect and better robustness in removing noise interference in comparison with the ensemble empirical mode decomposition(EEMD)method.

关 键 词：故障诊断 多尺度形态滤波 滚动轴承 振动信号 特征提取 

分 类 号：TH133.3[机械工程—机械制造及自动化]