摘要
滚动轴承在实际工况下的故障信号和故障信息常常淹没于噪声中,传统的故障特征提取方法很难有效提取出轴承故障特征信息。因此,采用时间固有尺度分解(ITD)和核独立分量分析(KICA)相结合的信噪盲分离分析法降噪。对轴承信号进行ITD分解,根据相关系数将分解得到的PRC分量重组以及构建虚拟噪声通道,利用KICA解混实现故障信号与噪声信号分离,对信噪分离后的有效分量信号做包络谱的分析。通过仿真及轴承故障实验分析和对比表明,该方法能有效提取轴承的故障特征。
The fault signal and information of rolling bearing is often drowned in the noise,the traditional extraction of bearing fault feature methods are difficult to effectively extract the bearing fault feature. The SNR of blind source separation,combing the inherent time scale decomposition(ITD) and independent component analysis(ICA) method is used to reduce noise. The fault signal is decomposed by ITD. The PRC component will be decomposed by correlation coefficient criterion restructuring as the virtual noise channel. The KICA is used to realize the separation of fault signal and noise signal,then analysis on the effective fault signal envelope is carried out. The simulation and comparison of experiments of rolling bearing fault analysis indicates that this method is effective to extract the bearing fault feature.
出处
《机械传动》
CSCD
北大核心
2018年第1期83-87,共5页
Journal of Mechanical Transmission
基金
国家重点研发计划项目(2016YFF0203100)
