A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity

Abstract

In the fast kurtogram (FK), kurtosis is used as an indicator to locate the fault frequency band, and is widely aplied to fault diagnosis. However, kurtosis has been proven to favor a single large impulse rather than the required small fault characteristics, especially in the strong interference environment. To eliminate the impact of large-amplitude impact and further improve the accuracy of fault extraction, a method based on generalized nonlinear spectral sparsity (GNSS) is proposed for fault diagnosis of bearings. First, Z-score normalization and generalized nonlinear sigmoid activation function are used for signal preprocessing, and the scale distribution of the signal will be changed to eliminate the effects of large amplitude shocks under noisy environment. Then, to improve the sparsity measure capability, an improved L3/2 norm is used to replace kurtosis as the basis for selecting the best resonance frequency band. Finally, the effectiveness of the GNSS is verified by simulation data and experimental data. Compared with FK method, the performance of fault extraction of the proposed method is significantly improved, especially for the interference of abnormal impact.