强背景噪声振动信号中滚动轴承故障冲击特征提取

针对机械早期故障引起的冲击特征微弱,易受强背景信号和噪声的干扰而难以提取的问题,提出一种奇异值分解(Singular Value Decomposition,SVD)差分谱与S变换相结合的微弱冲击特征提取方法。将原始信号构造成Hankel矩阵,采用SVD对重构矩阵进行分解;利用奇异值差分谱确定降噪阶次进行降噪;采用S变换对降噪后的信号进行时频分析,提取信号中的微弱冲击特征信息。通过数值仿真和实际轴承故障数据的对比,表明该方法可有效辨别轴承振动信号中故障引起的早期微弱冲击特征,为轴承故障诊断提供先验信息。

The impact features extraction of rolling bearing under strong background noise

Aiming at the problem that the impact of early mechanical failure is weak and it is difficult to extract due to strong background signal and noise interference,a weak impact feature extraction method combining singular value decomposition(SVD)differential spectrum and S-transform is proposed.The original signal is constructed into a Hankel matrix,and the reconstruction matrix is decomposed by SVD.The noise reduction order is determined by singular value difference spectrum for noise reduction.The S-transformation is used to analyze the time-frequency of the denoised signal,the weak impact characteristic information of the signal is extracted.The comparison between numerical simulation and actual bearing fault data shows that the method can effectively distinguish the early weak shock characteristics caused by faults in the bearing vibration signal.It can provide a priori information for the bearing fault diagnosis.