基于信息融合与FastICA的轴承故障提取方法

针对振动传感器监测信号易受噪声干扰的问题,提出一种基于FastICA算法与信息融合的轴承故障诊断方法。算法对各通道测得的信号采用FastICA算法进行降噪处理,采用自适应线性加权算法对降噪后信号进行数据层信息融合,最后基于谱峭度指标设计自适应带通滤波器,进行特征提取。此方法解决了低信噪比条件下的轴承故障特征提取问题。使用了仿真和实验轴承故障信号验证了算法的有效性。

Bearing fault diagnosis method based on information fusion and fast ICA

Here, aiming at the problem of signals monitored by vibration sensors being easy to be interfered by noise, a bearing fault diagnosis method based on fast ICA algorithm and information fusion was proposed.Firstly, this method used the fast ICA algorithm to de-noise signals measured at each channel.Then an adaptive linear weighted fusion algorithm was used to perform data layer information fusion for the de-noised signals.Finally, an adaptive band-pass filter was designed based on the spectral kurtosis index to extract features.This method solved bearing fault feature extraction problems under the condition of low SNR.Simulated and actual test bearing fault signals were used to verify the effectiveness of the proposed method.