Impulses Recovery Technique Based on High Oscillation Region Detection and Shifted Rank-1 Reconstruction—Its Application to Bearing Fault Detection

Abstract

The health status of bearing is directly related to the safe operation of rotating machinery. Bearing fault detection technology is of great significance to reduce or eliminate safety accidents. Singular value decomposition (SVD), as an effective low-rank approximation tool of data matrix, is widely used in bearing fault detection. The construction of trajectory matrix and the selection of singular value are two important factors that affect the performance of SVD-based fault diagnosis methods. In this paper, a new matrix low-rank approximation tool similar to SVD, shifted rank-1 reconstruction (SR1R), is introduced for fault diagnosis. SR1R uses periodic segment matrix (PSM) as trajectory matrix, and its signal reconstruction does not need to select singular value. In addition, a high oscillation region (HOR) detection method based on variance evaluation is proposed and applied to signal reconstruction of SR1R. The fault impulse detection method combining these two methods is called HOR-SR1R. The proposed method cannot only detect the fault impulses, but also suppress the noise between adjacent fault impulses. By contrast, the traditional SVD cannot eliminate the noise between adjacent fault impulses. The effectiveness of proposed method is validated by simulated signals, wheelset bearing data and open data set. Compared with two SVD-based fault diagnosis methods, Hankel matrix-based SVD and PSM-based SVD, the superiority of the proposed method is highlighted.