S变换时频谱SVD降噪的冲击特征提取方法

为了从滚动轴承故障振动信号中提取出冲击特征,以进行轴承故障诊断,提出基于S变换时频谱奇异值分解(SVD)的信号降噪方法。S变换是一种信号时频表示方法,适合于处理与分析非平稳的冲击特征信号。在SVD降噪过程中,数据矩阵由信号的S变换谱系数构成;奇异值序列的置零阈值位置坐标可由奇异值差分谱最前面部分峰值群的最后一个峰值点序号来确定。最后对降噪的数据矩阵进行S逆变换,获得信号的时域冲击特征。仿真研究表明,基于S变换时频谱的SVD降噪方法可以成功地从低信噪比信号中提取出周期性的冲击特征。将本方法用于处理与分析滚动轴承故障振动信号,根据所提取出的冲击特征出现频率,能够方便有效地实现轴承相关故障的诊断。

Impact feature extracting method based on S transform time-frequency spectrum denoised by SVD

In order to extract the impact feature from rolling bearing fault vibration signal, which is significant for bearing fault diagnosis, a signal denoising method based on SVD (Singular Value Decomposition) of S transform time-frequency spectrum is proposed. S transform is a means of signal time-frequency representation and particularly suitable for processing the non-stationary signal with impact feature. During SVD denoising, the target data matrix is composed of S transform spectrum coefficients. The position of the threshold singular value, be less than or equal to which the singular value will be set zero, can be determined by the last peak index of the peaks swarm in singular value difference spectrum. Finally, inverse S transform of the data matrix resulted from SVD denoising is made to reconstruct the time domain impact feature. The simulation results show that the proposed method can successfully extract the periodic impact feature from low SNR signal. In the processing of the rolling bearing fault vibration signals, this method is able to obtain the impact feature frequency, which can be used to diagnosis bearing relevant faults effectively.