改进SpotTune迁移学习的风电机组状态监测

doi:10.19595/j.cnki.1000-6753.tces.260878

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Abstract

Wind turbine maintenance costs account for a considerable proportion of total expenditure in the wind energy industry. To reduce economic losses caused by unexpected failures and downtime, real-time condition monitoring is essential. Spatio-temporal graph models can effectively extract temporal features from sensor data and spatial correlations among turbine components, but their performance usually relies on abundant training data. Newly commissioned turbines, however, often lack sufficient operating data. To address this issue, transfer learning is introduced to leverage well-labeled healthy data from source-domain turbines for target-domain modeling. Since conventional fine-tuning usually adjusts only the final layer and cannot adequately adapt to distribution differences between source and target domains, this paper proposes an improved SpotTune strategy. It preserves the discriminative knowledge learned from the source domain while improving adaptability to target-domain operating characteristics.
First, time-series samples are constructed from turbine operating data through data cleaning, normalization, and sliding-window segmentation. A graph is then built according to the physical connections among turbine components to embed prior knowledge and help the graph neural network learn spatial relationships among sensor nodes. Next, a source-domain spatio-temporal graph model is trained using healthy operating data, and its parameters are saved for transfer. These parameters are transferred to the frozen and fine-tunable modules of the target-domain model. The frozen module retains source-domain knowledge, whereas the fine-tunable module is adaptively updated using limited target-domain data. Meanwhile, a policy network generates layer-wise decision weights for the two modules, dynamically balancing knowledge retention and target-domain adaptation. Finally, the fine-tuned model performs node-level online condition monitoring to identify sensor deviations and support fault propagation analysis.
The method is validated on both a generated dataset and a public dataset. The main conclusions are as follows. (1) The amount of target-domain training data significantly affects monitoring performance. As training data decrease, MSE_all, RMSE_all, and MAE_all increase, indicating lower prediction accuracy and reliability. Under data-insufficient conditions, the model also struggles to form complete fault propagation chains, weakening its ability to determine fault range, severity, and source. (2) The improved SpotTune method effectively alleviates insufficient target-domain data and cross-unit distribution differences. It provides fault warnings 13 h in advance on the generated dataset and detects abnormal conditions at an early stage on the public dataset. Compared with the non-transfer and top-layer fine-tuning methods, it achieves higher prediction accuracy in both single-source and dual-source scenarios. On the public dataset, it reduces target-domain validation MSE_all, RMSE_all, and MAE_all by 92.9%, 74.7%, and 78.4%, respectively, relative to the non-transfer method, and by 82.4%, 60.3%, and 65.7% relative to top-layer fine-tuning. It also supports more reliable formation of fault propagation chains, improving the interpretability of early-warning results. (3) Compared with top-layer, partial-layer, and full-layer fine-tuning, the improved SpotTune method achieves higher prediction accuracy and demonstrates effectiveness and superiority in cross-unit transfer prediction. (4) Compared with representative transfer learning methods, its RMSE_all is reduced by 44.1% and 30.9% relative to Deep CORAL and DANN on the generated dataset, and by 16.2% and 16.4%, respectively, on the public dataset. These results confirm that the proposed method effectively improves the predictive performance of target-domain models.

Key words： Wind turbines status monitoring spatiotemporal graph model SpotTune transfer learning

Received: 03 June 2026

PACS:	TM315
	TH17

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	Pei Zhi
	Zhu Jingxin

Cite this article:

Pei Zhi,Zhu Jingxin. Improving SpotTune Transfer Learning for Wind Turbine State Monitoring[J]. Transactions of China Electrotechnical Society, 0, (): 260878-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.260878 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/260878

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