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

Yizhe Cai; Yan Lu

doi:10.1117/12.3054168

11 December 2024 The research on bearing fault feature extraction based on improved morphological component analysis sparse decomposition algorithm

Yizhe Cai, Yan Lu

Author Affiliations +

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134451X (2024) https://doi.org/10.1117/12.3054168
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yizhe Cai and Yan Lu "The research on bearing fault feature extraction based on improved morphological component analysis sparse decomposition algorithm", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134451X (11 December 2024); https://doi.org/10.1117/12.3054168

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