Meta-action unit fault identification based on multisource information fusion and D-S evidence theory

Hongyu Ge; Tianyu Luo

doi:10.1117/12.3014948

4 March 2024 Meta-action unit fault identification based on multisource information fusion and D-S evidence theory

Hongyu Ge, Tianyu Luo

Author Affiliations +

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129812Y (2024) https://doi.org/10.1117/12.3014948
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Aiming at the problem of limited fault information of a single sensor and low fault identification rate in the meta-action unit's fault identification process, a meta-action unit's fault identification method based on multisource information fusion and D-S evidence is proposed. Firstly, the fault identification framework of the mechanical element action unit is established, and multisource information acquisition and pre-processing are carried out on the built signal acquisition test platform of the mechanical element action unit; Then, the grey correlation method is used to analyze the correlation degree of the multisource information of the meta-action unit, and the multisource information fusion of the meta-action unit is realized based on the covariance cross method; Finally, the D-S evidence theory is used to complete the fault identification of the meta-action unit. This method is applied to the fault identification of the ball screw element action unit and compared with the single vibration signal and the traditional information fusion method to verify the accuracy and superiority of this metho

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

Citation Download Citation

Hongyu Ge and Tianyu Luo "Meta-action unit fault identification based on multisource information fusion and D-S evidence theory", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129812Y (4 March 2024); https://doi.org/10.1117/12.3014948

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