DAMAGE IDENTIFICATION OF WIND TURBINE PITCH BEARING BASED ON AFMD AND SVDD

Wang Xiaolong; Zhang Bowen; Jin Hanwei; Fu Ruiqi; Yang Xiubin; Wu Peng

doi:10.19912/j.0254-0096.tynxb.2023-1743

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 514-523. DOI: 10.19912/j.0254-0096.tynxb.2023-1743

DAMAGE IDENTIFICATION OF WIND TURBINE PITCH BEARING BASED ON AFMD AND SVDD

Wang Xiaolong¹, Zhang Bowen¹, Jin Hanwei¹, Fu Ruiqi¹, Yang Xiubin¹, Wu Peng²

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Abstract

Aiming at the problem of damage identification of pitch bearings of wind turbine, a damage identification method based on adaptive feature mode decomposition and singular value decomposition denoising is proposed. Firstly, Runge kutta optimization is used to search for the frequency band number and the filter length parameters of the feature mode decomposition algorithm, then the optimal combination of influencing parameters is determined and the original vibration signal is processed by adaptive feature mode decomposition, and the mode component containing rich feature information is extracted. Subsequently, the envelope signal of the extracted mode component is calculated and the singular value decomposition denoising is further operated to enhance the signal-to-noise ratio of the envelope signal. Finally, the theoretical damage characteristic frequency and the frequency component with prominent amplitude in the envelope spectrum are compared to judge the fault damage of the pitch bearing. Experimental data analysis results show that the proposed method can effectively extract weak feature information from complex original vibration signal and accurately identify the damage part of pitch bearing, which has certain engineering reference value.

Key words

wind turbines / pitch bearing / damage identification / adaptive feature mode decomposition / singular value decomposition denoising

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Wang Xiaolong, Zhang Bowen, Jin Hanwei, Fu Ruiqi, Yang Xiubin, Wu Peng. DAMAGE IDENTIFICATION OF WIND TURBINE PITCH BEARING BASED ON AFMD AND SVDD[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 514-523 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1743

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