An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

Precise detection of fault characteristics in rolling bearings is imperative for machine health management. However, due to the presence of interfering components including noise and periodic components caused by vibration sources, the extraction of weak fault-related information cannot be achieved precisely. In this study, we propose an optimized Laplacian of Gaussian (LoG) filtering technique to handle this issue. The proposed algorithm utilizes the envelope entropy and Gini of square envelope as an objection function to optimize two important parameters, namely standard deviation and filter order of the LoG filter, through an improved sparrow search algorithm (SSA) named adaptive spiral flying SSA. Afterward, the LoG filtering method with the optimal parameters is employed to filter the raw vibration data. Finally, the filtered signal undergoes envelope analysis for fault feature detection. A simulated test and two case studies demonstrate the effectiveness and superiority of the LoG technique.