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Volume 6
Issue 12
10.3390/eng6120333
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Integrated TCN-GRU Deep Learning Approach for Fault Detection in Floating Offshore Wind Turbine Drivetrains

by
Yangdi Luo
1,2,
Yaozhen Han
1,2,*,
Fei Song
2,
Bingxin Xue
2 and
Yanbin Yin
3
1
Shandong Key Laboratory of Technologies and Systems for Intelligent Construction Equipment, Shandong Jiaotong University, Jinan 250357, China
2
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
3
Shandong Chuangxin Electric Power Technology Co., Ltd., Jinan 250000, China
*
Author to whom correspondence should be addressed.
Eng 2025, 6(12), 333; https://doi.org/10.3390/eng6120333 (registering DOI)
Submission received: 20 October 2025 / Revised: 18 November 2025 / Accepted: 20 November 2025 / Published: 22 November 2025
(This article belongs to the Special Issue Engineering Applications of Power Electronics in Renewable Energy Systems)
Versions Notes

Abstract

In the complex operational environment of offshore wind turbines, the drivetrain system faces multiple uncertainties including wind speed fluctuations, wave disturbances, and dynamic coupling effects, which significantly increase the difficulty of fault identification. To address this challenge, this paper proposes a deep learning model integrating Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) to enhance fault detection capability. The TCN module extracts multi-scale temporal features from vibration signals, while the GRU module captures long-term dependencies in drivetrain degradation patterns. The study utilizes a publicly available Zenodo dataset containing simulated acceleration signals from a 5-MW reference drivetrain under three offshore conditions, covering healthy and faulty states of the main shaft, high-speed shaft, and planet bearings. Experimental validation under different operational conditions demonstrates that the proposed TCN-GRU model outperforms baseline models in terms of accuracy, precision, and recall.
Keywords: offshore wind turbine; drivetrain system; fault detection; deep learning; TCN-GRU offshore wind turbine; drivetrain system; fault detection; deep learning; TCN-GRU

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MDPI and ACS Style

Luo, Y.; Han, Y.; Song, F.; Xue, B.; Yin, Y. An Integrated TCN-GRU Deep Learning Approach for Fault Detection in Floating Offshore Wind Turbine Drivetrains. Eng 2025, 6, 333. https://doi.org/10.3390/eng6120333

AMA Style

Luo Y, Han Y, Song F, Xue B, Yin Y. An Integrated TCN-GRU Deep Learning Approach for Fault Detection in Floating Offshore Wind Turbine Drivetrains. Eng. 2025; 6(12):333. https://doi.org/10.3390/eng6120333

Chicago/Turabian Style

Luo, Yangdi, Yaozhen Han, Fei Song, Bingxin Xue, and Yanbin Yin. 2025. "An Integrated TCN-GRU Deep Learning Approach for Fault Detection in Floating Offshore Wind Turbine Drivetrains" Eng 6, no. 12: 333. https://doi.org/10.3390/eng6120333

APA Style

Luo, Y., Han, Y., Song, F., Xue, B., & Yin, Y. (2025). An Integrated TCN-GRU Deep Learning Approach for Fault Detection in Floating Offshore Wind Turbine Drivetrains. Eng, 6(12), 333. https://doi.org/10.3390/eng6120333

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