基于小波旁瓣相消器的轴承故障特征提取

Bearing Fault Feature Extraction Based on Wavelet Generalized Sidelobe Canceller

为解决强背景噪声下声信号提取的轴承故障特征不显著问题,提出一种基于小波旁瓣相消器的故障特征提取方法。该方法利用小波滤波器组将含噪故障轴承声信号变换到小波域,进行小波域阵列广义旁瓣相消自适应波束形成,再通过小波滤波器组重构增强后的故障轴承信号,最后对重构增强后的信号进行包络解调并提取故障特征频率进行故障诊断。实验结果表明,该方法能够在强背景噪声下有效提取滚动轴承故障特征,并且相较于传统的延时求和波束形成器具有更好的降噪和故障特征增强效果。

In order to solve the problem of insignificant bearing fault features extracted from acoustic signals under strong background noise, a fault feature extraction method based on wavelet beamforming is proposed.This method uses a wavelet filter bank to transform the acoustic signal of a noisy faulty bearing into the wavelet domain and performs an array of wavelet domain generalized sidelobe cancellation adaptive beamforming, and then reconstructs the enhanced fault through the wavelet filter bank bearing acoustic signal, and finally envelops demodulation of the reconstructed and enhanced signal and extract the fault characteristic frequency for fault diagnosis.The experimental results show that the method can effectively extract the fault features of rolling bearings under strong background noise, and has better noise reduction and fault feature enhancement effects than the traditional time-delay summation beamformer.