针对强噪声导致卷积盲源分离故障源信号估计精度较低的问题,提出一种结合最小均方算法(least mean square,简称 LMS)滤波和卷积盲分离(robust multichannel blind deconvolution,简称 RobustMBD)的滚动轴承复合故障诊断方法.首先,利用LMS滤波对含噪的轴承故障信号进行去噪预处理,降低噪声对故障信号的影响;其次,通过构建时滞关联模型将卷积混合模型转换为瞬时混合模型,并以归一化峭度为分离判据,采用精确线搜索替代迭代搜索,得到卷积盲分离方法鲁棒多通道盲解卷积;然后,对降噪后的复合故障信号采用鲁棒多通道盲解卷积进行肓源分离,得到轴承的独立故障信号;最后,通过仿真和滚动轴承试验数据对提出的滚动轴承复合故障诊断方法进行了验证.结果表明,与传统鲁棒多通道肓解卷积相比,在强噪声情况下,提出的方法能够有效分离出所有的故障信号.
Bearing Fault Diagnosis Method Combining LMS Filtering and Blind Deconvolution
Strong noise will lead to low estimation accuracy of convolution blind source separation of fault source signals.To solve this problem,a composite fault diagnosis method for rolling bearings based on the least mean square(LMS)filtering and convolutional blind separation is proposed.This method uses LMS filtering to preprocess the noisy bearing fault signals.With the convolution blind separation method called robust multi-channel blind deconvolution(RobustMBD)to separate the composite fault signals after noise reduction,the in-dependent fault signals of the bearing are obtained.The RobustMBD constructs a time-delay correlation model to extend the convolution condition to the instantaneous condition,using the normalized kurtosis and the precise line searching.This method is verified by simulation and rolling bearing test data.The results show that com-pared with the traditional RobustMBD,the proposed method can effectively separate all fault signals under strong noise conditions.
rolling bearingfault diagnosisleast mean square(LMS)filteringblind source separation of con-volution
NETL
NSTL
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