Fault diagnosis method for rolling bearings based on segment tensor rank-(L, L, 1) decomposition

Abstract

The rolling bearing is the most basic key component of rotating machinery, and has high failure rates. In this paper, a fault diagnosis method based on segment-tensor rank-(Lr, Lr, 1) decomposition for rolling bearings is proposed. First, the IMF-SVD (intrinsic mode function, IMF; singular value decomposition, SVD) method is utilized to estimate the source number of the vibration observation signals. Then, each observation signal is reshaped into a segment matrix by a segmentation method, and then stacked into a third order segment tensor. Using the segment tensor rank-(Lr, Lr, 1) decomposition, a set of sub tensors that correspond to the source signals can be obtained. Next, the source signal can be reconstructed using mode-1 and mode-2 of the corresponding sub tensor. Finally, through the envelope demodulation of the source signals, the fault characteristic frequency (FCF) is extracted to achieve the purpose of fault diagnosis. The results of simulation and actual data analysis verified the effectiveness of the proposed method in rolling bearing fault diagnosis.