Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis
Mao Ge, Yong Lv, Yubo Ma
Abstract
Tensor decomposition technique has been widely used in multichannel signals processing for its distinct superiority. The early fault feature signal of bearing is weak and easily inundated by ambient noise. In addition, the interference signals generated by other mechanical components also have a serious impact on the result of fault diagnosis. Aiming at the above issues, and built on the methods of generalized non-convex tensor robust principal component analysis (GNCTRPCA) and tensor singular value kurtosis (TSVK), this paper introduces a new fault diagnosis technique for multichannel bearing signals. First, the attractor tensor is formed by reconstructing the acquired multichannel signals in phase space. Second, based on tensor singular value decomposition (TSVD), the tensor robust principal component analysis (TRPCA) can provide a favorable noise reduction performance. However, TRPCA usually lowers the amplitude of useful singular value tubes (SVTs). To tackle this problem, the GNCTRPCA method is proposed to avoid the amplitude reduction. Third, a new TSVK method is introduced to determine the reconstructed order of SVTs, so as to extract the multichannel fault feature signals. Finally, the bearing fault type can be identified by comparing the peak frequencies of the extracted signals with the theoretical fault-related frequencies. Simulation analysis and experiment studies verify the principle and effectiveness of the proposed technique.