基于IWT-CEEMD-cICA的滚动轴承故障诊断方法  

Fault Diagnosis Method for Rolling Bearings Based on IWT-CEEMD-cICA

作　　者：韩博跃 郝如江 金治彬 王万仁 HAN Boyue;HAO Rujiang;JIN Zhibin;WANG Wanren(School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;China Railway 22nd Bureau Group Rail Engineering Co.,Ltd.,Beijing 100040,China)

机构地区：[1]石家庄铁道大学机械工程学院,石家庄050043 [2]中铁二十二局集团轨道工程有限公司,北京100040

出　　处：《轴承》2020年第2期38-42,共5页Bearing

基　　金：国家自然科学基金项目(51375319);河北省引进留学人员项目(CL201721)。

摘　　要：为在强噪声下准确利用振动信号进行轴承微弱故障的诊断,提出了一种基于改进小波阈值、互补集合经验模态分解和约束独立分量分析的故障诊断方法。首先,对单通道振动信号进行改进小波阈值降噪预处理,提高输入信号的信噪比;然后,进行CEEMD处理以实现降噪及单通道扩展,基于峭度值和相关系数选取有效固有模态函数并将其作为盲源分离的输入信号;最后,通过cICA方法提取目标振动信号,识别故障特征。In order to accurately diagnose weak faults of bearings by vibration signal under strong noise,a fault diagnosis method based on IWT-CEEMD-cICA is proposed.Firstly,the improved wavelet threshold denoising preprocessing is carried out for single-channel vibration signal to improve signal-to-noise ratio of input signal.Then,the CEEMD is used to realize denoising and single-channel expansion,and an input signal with effective intrinsic mode function is selected based on the kurtosis value and related coefficients and separated as a blind source.Finally,the target vibration signal is extracted by cICA method to identify fault features.

关 键 词：滚动轴承 故障诊断 信噪比 小波变换 阈值 CEEMD cICA 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH113.1