Fault diagnosis of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM

Haiyang Liu; Xianwen Zeng

doi:10.1117/12.3052685

11 December 2024 Fault diagnosis of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM

Haiyang Liu, Xianwen Zeng

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134450E (2024) https://doi.org/10.1117/12.3052685
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

In order to improve the accuracy of diagnosis, a fault diagnosis and research method of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM is proposed. The method firstly uses ICEEMDAN to decompose the sampled data signals, then uses PE to extract the features, and finally inputs the features into Grey Wolf algorithm (GWO) to optimize the Least Squares Support Vector Machine (LSSVM) diagnostic model to train the optimal parameters. A fault diagnosis model is built to identify common faults in gearbox bearings accurately. Combined with the simulation experimental data, the diagnosis accuracy is 97.67%, which proves the feasibility of the scheme, and provides an effective method for improving the accuracy of the gearbox bearing fault diagnosis

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

Citation Download Citation

Haiyang Liu and Xianwen Zeng "Fault diagnosis of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134450E (11 December 2024); https://doi.org/10.1117/12.3052685

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