Study on the method of motor short-circuit fault based on the improved random forest

Baoliang Li; Aiming Zhao

doi:10.1117/12.3049386

27 September 2024 Study on the method of motor short-circuit fault based on the improved random forest

Baoliang Li, Aiming Zhao

Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 1328403 (2024) https://doi.org/10.1117/12.3049386
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China

Abstract

This study presents an improved random forest algorithm for motor short-circuit fault diagnosis. The classification accuracy of the model is significantly improved by data preprocessing, time and frequency domain feature extraction, and parameter optimization. The experimental results show that the improved random forest algorithm performs well in the fault classification task and has obvious advantages over the traditional methods. This study provides an effective solution for motor fault diagnosis and provides a reference for further research in related fields.

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

Citation Download Citation

Baoliang Li and Aiming Zhao "Study on the method of motor short-circuit fault based on the improved random forest", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 1328403 (27 September 2024); https://doi.org/10.1117/12.3049386

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