基于MED-ITD和CICA的滚动轴承故障诊断  

Fault Diagnosis for Rolling Bearings Based on MED-ITD and CICA

作　　者：邢亚航 郝如江 余忠潇 XING Yahang;HAO Rujiang;YU Zhongxiao(School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]石家庄铁道大学机械工程学院,石家庄050043

出　　处：《轴承》2021年第1期39-45,共7页Bearing

基　　金：国家自然科学基金项目(51375319)。

摘　　要：针对滚动轴承故障诊断在实际中受到噪声影响,故障难以识别的问题,提出了一种基于最小熵反褶积(MED)和固有时间尺度分解(ITD),并结合约束独立分量分析(CICA)的方法。首先,通过MED对轴承故障信号进行降噪,以滤除噪声信号,增强信号冲击成分;然后,通过ITD对降噪信号进行分解,选择合适的筛选分量进行重构;最后,采用CICA方法对重构信号进行盲源分离,通过希尔伯特包络谱进行分析提取出准确的故障信号,并经过试验验证了所提方法的有效性。The fault diagnosis of rolling bearings is affected by noise in practice,and the fault is difficult to identify.A method is proposed based on minimum entropy deconvolution(MED)and intrinsic time scale decomposition(ITD)combined with constrained independent component analysis(CICA).Firstly,the bearing fault signal is denoised by MED to filter out noise signal,and the impact component of signal is enhanced.Then,the denoised signal is decomposed through ITD,and the appropriate screening component is selected for reconstruction.Finally,the CICA method is used for blind source separation of reconstructed signal,and the accurate fault signals are extracted through Hilbert envelope spectrum analysis.The effectiveness of the proposed method is verified through experiments.

关 键 词：滚动轴承 故障特征 包络谱 最小熵反褶积 时间尺度分解 约束独立分量分析 

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]