A Novel Weak Fault Diagnosis Method Based on Sparse Representation and Empirical Wavelet Transform for Rolling Bearing

Rotating machinery is widely used in industry. However, it works in tough environment, which makes the fault features extraction difficult. In the last few years, sparse representation, as a kind of effective feature extraction method, has great promise in industrial diagnosis. As for the traditional sparse representation, the greedy algorithm used to update the sparse coefficients is prone to produce local optimal solution and lead to over-fitting. In addition, if the signal contains a lot of redundant information, the basis learned by the traditional method cannot well represent the fault signal. Aim at the above questions, a novel weak fault diagnosis method based on sparse representation and empirical wavelet transform (EWT) is proposed in this paper. Gaussian filter is exploited to process the signal spectral, which can make the signal spectral smooth and the spectrum division more precise. Next, the signal spectrum is divided into N parts based on EWT. Then the kurtosis is utilized to screen out the optimal part of spectrum, which will be exploited to obtain a sparse basis. The constraint with nuclear norm is applied to remove the redundant component of the basis. Finally, the LASSO with elastic net, is employed to get sparse signal, the envelope spectrum is used to extract fault feature. Experimental results show that this method is better than traditional sparse representation using learning dictionary.