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Applied Sciences
Volume 15
Issue 23
10.3390/app152312726
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network

by
Chiheng Huang
1,
Wenxian Yang
1,*,
Oussama Graja
1,
Fang Duan
2,
Zeqi Wei
3 and
Liuyang Zhang
3
1
School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
2
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
3
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12726; https://doi.org/10.3390/app152312726 (registering DOI)
Submission received: 30 October 2025 / Revised: 23 November 2025 / Accepted: 27 November 2025 / Published: 1 December 2025
(This article belongs to the Special Issue Vibration Control of On- and Off-Shore Wind Turbines)
Versions Notes

Abstract

Traditional wind turbine drivetrain health assessment generally depends on feature extraction guided by expert experience and prior knowledge. However, the effectiveness of this approach is often limited when such knowledge is insufficient or when fault features are obscured by high levels of ambient noise. In response to these issues, this study proposes a new data-driven framework that combines intelligent frequency band identification with a deep learning architecture. In the proposed approach, vibration signals from the bearings are transformed into their spectral representation, and the frequency spectrum is divided into multiple frequency bands. The relative importance of each band is evaluated and ranked using XGBoost, enabling the selection of the most informative features and significant dimensionality reduction. A hybrid CNN–Transformer model is then employed to combine local feature extraction with global attention mechanisms for accurate fault classification. Experimental evaluations using two open-source datasets indicate that the proposed framework achieves high classification accuracy and rapid convergence, offering a robust and computationally efficient solution for wind turbine drivetrain fault diagnosis.
Keywords: wind turbine; fault diagnosis; XGBoost; drive train; deep learning wind turbine; fault diagnosis; XGBoost; drive train; deep learning

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MDPI and ACS Style

Huang, C.; Yang, W.; Graja, O.; Duan, F.; Wei, Z.; Zhang, L. Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network. Appl. Sci. 2025, 15, 12726. https://doi.org/10.3390/app152312726

AMA Style

Huang C, Yang W, Graja O, Duan F, Wei Z, Zhang L. Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network. Applied Sciences. 2025; 15(23):12726. https://doi.org/10.3390/app152312726

Chicago/Turabian Style

Huang, Chiheng, Wenxian Yang, Oussama Graja, Fang Duan, Zeqi Wei, and Liuyang Zhang. 2025. "Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network" Applied Sciences 15, no. 23: 12726. https://doi.org/10.3390/app152312726

APA Style

Huang, C., Yang, W., Graja, O., Duan, F., Wei, Z., & Zhang, L. (2025). Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network. Applied Sciences, 15(23), 12726. https://doi.org/10.3390/app152312726

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