Bearing Fault Diagnosis Using Convolutional Sparse Representation Combined With Nonlocal Similarity

Abstract

Conducting fault diagnosis to bearing is of great significance to ensure their 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 paper, a novel fault diagnosis algorithm is proposed by using convolutional sparse representation under the non-local similarity of vibration signal. Firstly, the denoised similar subblocks is collected in non-local region with correlation coefficient to speed up the training process of adaptive filters and reconstruct the original signal more closely. Secondly, to remove noise contained in each component, a denoising algorithm based on adaptive threshold is employed to shrink the coefficients of each component decomposed by convolutional sparse representation. Then, a measure function indicating the activity of fault frequency is designed to select the optimal signal subband which contains the main fault information. Finally, the envelope spectrum is calculated to detect the fault frequency. In order 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 its harmonics and exhibits advantages in fault frequency detection compared with other fault diagnosis methods.