Fault diagnosis of wind turbine bearings based on WL-MF

Yunfan Yang; Caiping Xi; Zhibo Gao

doi:10.1117/12.2672864

25 April 2023 Fault diagnosis of wind turbine bearings based on WL-MF

Yunfan Yang, Caiping Xi, Zhibo Gao

Proceedings Volume 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022); 1259811 (2023) https://doi.org/10.1117/12.2672864
Event: Eighth International Conference on Energy Materials and Electrical Engineering (ICMEE 2022), 2022, Guangzhou, China

Abstract

Wind turbines are more prone to faults due to long operation in harsh environments. It is necessary to implement fault diagnosis for wind turbines to ensure the stability and reliability of the circuit system safety as well as to reduce the probability of faults. In this paper, a fault detection method based on Wavelet Leaders (WL) and limit learning machine is proposed to achieve fault prediction for fault-prone bearings. The WL algorithm is first applied to analyze the multiple fractality of several different states of faulty bearings, select the stable fault parameter features for extraction and finally use the ELM classification tool to achieve the classification of faults. The experimental results demonstrate that this method achieves fault classification detection and provides a new idea for faulty bearing detection.

Citation Download Citation

Yunfan Yang, Caiping Xi, and Zhibo Gao "Fault diagnosis of wind turbine bearings based on WL-MF", Proc. SPIE 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022), 1259811 (25 April 2023); https://doi.org/10.1117/12.2672864

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