A novel bearing diagnosis method under random impact disturbance based on the nonlinear convolutional sparse filtering

The fault diagnostic approach based on sparse optimization has been receiving considerable attention. Which shows superior robustness and noise adaptability. However, in the real working environment, the collected bearing signals are often accompanied by random impact interference owing to alterations in working circumstances and load mutations. In this paper, the nonlinear sparse metric is used to reduce the interference of random shock excitation (FNCSF). The nonlinear activation has different activation coefficients for different amplitudes, which can change the sparsity distribution of the raw data. Firstly, the influence of different nonlinear activation functions on the diagnostic performance is studied. Then, in order to solve the scale inconsistency caused by nonlinear activation, the nonlinear function is created to the generalized form to further improve the noise adaptability of feature extraction. The second-order Gaussian fitting is used to improve the performance of the learning filter. Simulation and experimental results verify the performance of the proposed method. The results demonstrate that the suggested technique can significantly reduce the interference of random impact components.