Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation

• URL: http://arxiv.org/abs/2504.17709v1
• Date: Thu, 24 Apr 2025 16:14:04 GMT
• Title: Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation
• Abstract summary: We present a novel generative deep learning approach to make SCADA samples from one wind turbine lacking training data resemble SCADA data from wind turbines with representative training data.<n>Our findings show significantly improved fault diagnosis in wind turbines with scarce data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect anomalies and, eventually, operation faults. However, data-driven normal behavior models (NBMs) require a substantial amount of training data, as NBMs trained with scarce data may result in unreliable fault diagnosis. To overcome this limitation, we present a novel generative deep learning approach to make SCADA samples from one wind turbine lacking training data resemble SCADA data from wind turbines with representative training data. Through CycleGAN-based domain mapping, our method enables the application of an NBM trained on an existing wind turbine to one with severely limited data. We demonstrate our approach on field data mapping SCADA samples across 7 substantially different WTs. Our findings show significantly improved fault diagnosis in wind turbines with scarce data. Our method achieves the most similar anomaly scores to an NBM trained with abundant data, outperforming NBMs trained on scarce training data with improvements of +10.3% in F1-score when 1 month of training data is available and +16.8% when 2 weeks are available. The domain mapping approach outperforms conventional fine-tuning at all considered degrees of data scarcity, ranging from 1 to 8 weeks of training data. The proposed technique enables earlier and more reliable fault diagnosis in newly installed wind farms, demonstrating a novel and promising research direction to improve anomaly detection when faced with training data scarcity.

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