FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON ADAPTIVE SPECTRAL GRAPH WAVELET CONVOLUTIONAL NEURAL NETWORKS

Xu Zhicheng; Luo Shuo; Zhang Chuang; Jin Liang; Zhang Xian

doi:10.19912/j.0254-0096.tynxb.2024-1462

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 564-573. DOI: 10.19912/j.0254-0096.tynxb.2024-1462

FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON ADAPTIVE SPECTRAL GRAPH WAVELET CONVOLUTIONAL NEURAL NETWORKS

Xu Zhicheng^1,2, Luo Shuo^1,2, Zhang Chuang¹, Jin Liang^1,2, Zhang Xian^1~3

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Abstract

Traditional intelligent fault diagnosis methods face challenges in extracting reliable features, achieving high diagnostic accuracy, and providing feature interpretability under strong noise and complex operating conditions. To address these issues, this paper proposes an adaptive spectral graph wavelet convolutional neural network （ASGWCN） fault diagnosis method for the bearings in large rotating equipment,such as aircraft engines and wind turbines. The method considers the interactions between multiple sensors and converts vibration signals into graph-structured data. It integrates the adaptive spectral graph wavelet and re-weighted wavelet coefficient strategies,optimized by the Cheetah optimization algorithm,into the graph convolution layers,thereby constructing the ASGWCN. This approach dynamically adjusts the design parameters and decomposition levels of the spectral graph wavelet based on the characteristics of the vibration signal,enabling efficient multi-scale feature extraction. The different scale features are assigned different weights,enhancing the signal denoising and weak feature extraction capabilities. The method achieves end-to-end bearing fault diagnosis in situ under high noise environments,while also providing improved interpretability for the graph convolution process. The experimental results show that the proposed method exhibits excellent diagnostic performance and robustness under both high noise and complex working conditions.

Key words

wind turbines / bearings / fault diagnosis / graph networks / graph wavelet

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Xu Zhicheng, Luo Shuo, Zhang Chuang, Jin Liang, Zhang Xian. FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON ADAPTIVE SPECTRAL GRAPH WAVELET CONVOLUTIONAL NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 564-573 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1462

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