IMPROVED CNN-XGBOOST FUSION MODEL-BASED FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES UNDER GRAM ANGLE DIFFERENCE FIELD

Wang Yan; Wang Zijian; Zhong Xinqi; Liang Shiyu; Zhao Hongshan

doi:10.19912/j.0254-0096.tynxb.2025-0603

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 755-765. DOI: 10.19912/j.0254-0096.tynxb.2025-0603

IMPROVED CNN-XGBOOST FUSION MODEL-BASED FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES UNDER GRAM ANGLE DIFFERENCE FIELD

Wang Yan, Wang Zijian, Zhong Xinqi, Liang Shiyu, Zhao Hongshan

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Abstract

To further enhance the accuracy of fault diagnosis for wind turbine gearboxes, an intelligent diagnostic model based on the improved convolutional neural network （CNN） and extreme gradient boosting （XGBoost） fusion under the Gramian angular difference field （GADF） transformation is proposed. Firstly, the one-dimensional vibration signal data of the gearbox under complex environments are transformed into two-dimensional images through the GADF transformation, which retains the intrinsic structure and time-frequency characteristics of the signals. Secondly, an improved CNN method for multi-dimensional feature extraction of gearbox vibration images is proposed, where dual attention modules are introduced in the convolutional layers of the traditional CNN to enhance the model’s perceptual capabilities and increase the extraction of global information. The improvedβc-ACONC activation function which replaces the ReLU activation function is employed to selectively activate neurons, thereby enhancing the network's overall feature expression capabilities. Then, the integrated features are input into the XGBoost network optimized by ISSA hyperparameters, constructing a wind turbine gearbox fault diagnostic model based on the improved CNN-XGBoost fusion under the GADF. Finally, the performance of the model is verified using a laboratory wind turbine gearbox dataset, and the experimental results demonstrate that the model has a diagnostic accuracy of over 99%, exhibiting excellent fault recognition capabilities.

Key words

wind turbines / convolutional neural networks / fault diagnosis / Gramian angular difference field / XGBoost / gearbox

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Wang Yan, Wang Zijian, Zhong Xinqi, Liang Shiyu, Zhao Hongshan. IMPROVED CNN-XGBOOST FUSION MODEL-BASED FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES UNDER GRAM ANGLE DIFFERENCE FIELD[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 755-765 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0603

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