Bearing Fault Diagnosis Using Convolutional Sparse Representation Combined With Nonlocal Similarity

Conducting fault diagnosis of bearing is of great significance to ensure stable working and reduce economic losses of equipment in industrial manufacturing. However, the existing methods are not accurate enough to detect fault frequency in real operating conditions affected by noise. To detect the fault frequency more accurately, in this article, a novel fault diagnosis algorithm is proposed by using convolutional sparse representation (CSR) under the nonlocal similarity of the vibration signal. First, the denoised similar subblocks are collected in the nonlocal region with a correlation coefficient to speed up the training process of adaptive filters and reconstruct the original signal more closely. Second, to remove noise contained in each component, a denoising algorithm based on an adaptive threshold is employed to shrink the coefficients of each component decomposed by CSR. Then, a measure function indicating the activity of fault frequency is designed to select the optimal signal subband that contains the main fault information. Finally, the envelope spectrum is calculated to detect the fault frequency. To verify the performance of the proposed algorithm, we conduct a series of experiments on two public datasets and one real collected data and compare the results with some state-of-the-art fault diagnosis algorithms. Experimental results show that the proposed algorithm can accurately detect the fault frequency of rolling bearings and their harmonics and exhibit advantages in fault frequency detection compared with other fault diagnosis methods.