The research on bearing fault feature extraction based on improved morphological component analysis sparse decomposition algorithm

Modern machinery often possesses complex structures and operates in harsh environments, making it prone to failures. Vibration signals, containing crucial information about equipment operation status, are frequently affected by significant noise interference during the collection process, which results in the crucial components being overshadowed by noise, making fault characteristics difficult to extract. To address these issues, a sparse decomposition algorithm based on successive orthogonal matching pursuit and morphological component analysis is proposed for extracting fault features from rolling bearing vibration signals. This method utilizes two threshold screening criteria based on statistical principles, exhibiting strong noise resistance and consequently better recovery capabilities for signals under strong noise interference. Experimental analysis confirms the effectiveness of this method in extracting fault features from rolling bearings.