基于固有时间尺度分解与多尺度形态滤波的滚动轴承故障特征提取方法  

Fault Feature Extraction Method of Rolling Bearing Based on Intrinsic Time-scale Decomposition and Multiscale Morphological Filtering

作　　者：关焦月 田晶[1] 赵金明 富华丰 GUAN Jiao-yue;TIAN Jing;ZHAO Jin-ming;FU Hua-feng(Liaoning Key Laboratory of Advanced Measurement and Test Technology for Aircraft Propulsion Systems, Shenyang Aerospace University,Shenyang 110136,China;China Southern Airlines Company Limited Shenyang Aircraft Maintenance Overhaul Base, Shenyang 110169,China)

机构地区：[1]沈阳航空航天大学辽宁省航空推进系统先进测试技术重点实验室,沈阳110136 [2]中国南方航空股份有限公司沈阳维修基地,沈阳110169

出　　处：《科学技术与工程》2019年第14期178-182,共5页Science Technology and Engineering

基　　金：国家自然科学基金(11702177);辽宁省自然科学基金(20180550650);辽宁省教育厅项目(LN201710)资助

摘　　要：为了准确提取出滚动轴承的故障特征并对轴承状态进行评估,提出了一种固有时间尺度分解(intrinsic time-scale decomposition,ITD)与多尺度形态滤波相结合的滚动轴承故障特征提取方法。首先,采用ITD方法将滚动轴承故障信号分解成多个固有旋转分量(proper rotation,PR);然后,对比各个PR分量与原始信号的相关性;最后,采用多尺度形态滤波算法对相关性较大PR分量进行滤波降噪,并提取滚动轴承故障特征频率。采用所建立方法对轴承外圈故障和内圈故障实验数据进行分析。结果表明,所提出的故障特征提取方法能够有效抑制噪声,清晰准确地提取出滚动轴承故障特征频率。The intrinsic time-scale decomposition( ITD)-multiscale morphological filtering( MSMF),a novel fusing approach was proposed to extract fault feature and evaluate bearing condition. Firstly,the failure signal of rolling bearing is decomposed into multiple proper rotation( PR) by using ITD. Secondly,the correlation between the PR components and the original signal was compared. Finally,using MMF to filter and denoise for the large relativity of PR component,and extract the fault feature of the rolling bearing. The experimental data analyzed showed that the method proposed can effectively repress the noise and accurately extract the fault feature frequency of the rolling bearing.

关 键 词：固有时间尺度分解 形态滤波 滚动轴承 相关系数 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化] TP206[自动化与计算机技术—检测技术与自动化装置]