Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

• URL: http://arxiv.org/abs/2608.30323v1
• Date: Mon, 31 Aug 2026 06:40:03 GMT
• Title: Generative multi-domain transfer learning for fault detection in data-scarce wind turbines
• Abstract summary: We propose a multi-domain generative domain mapping approach to improve fault detection on data-scarce wind turbines.<n>Our approach can consistently outperform models trained on scarce data when less than 2 weeks of training data are available.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require sufficient fault-free training data to learn the normal operation behavior of turbines. Under data scarcity, for example in newly deployed wind turbines, these models may result in poor fault detection performance. In this work, we propose a multi-domain generative domain mapping approach based on Star Generative Adversarial Networks (StarGAN) to improve fault detection on data-scarce wind turbines. Our model maps SCADA measurements from a data-scarce turbine to resemble those of several data-rich turbines. By preserving the operational state during translation, faults occurring in a data-scarce domain can be mapped and detected by reliable pre-trained normal behavior models of data-rich domains. Highlighting the benefits of an ensemble fusion strategy, we show that under severe data scarcity our method can produce anomaly scores comparable to models trained on large representative datasets. Our approach can consistently outperform models trained on scarce data when less than 2 weeks of training data are available. With just 2 weeks of accumulated training data, we achieve an anomaly score similarity that is, on average, +16% higher than conventional fine-tuning, and +10% higher than single-source domain mapping. As a step towards unsupervised model selection, we propose a proxy metric that detects poor performance at training time, despite an absence of anomalies. Our study presents the potential and challenges of multi-domain mapping for wind turbine fault detection under unrepresentative training data.

Related papers

• Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data [0.0]
  This paper proposes an anomaly detection model that fuses a Transformer and a generative adversarial network.<n>It reduces the leakage detection rate of minor deviation anomalies by amplifying the reconstruction error.<n>It also uses autoregressive inference to extract multimodal features to enhance the stability and generalization ability of training.
  arXiv  Detail & Related papers  (2026-06-02T03:57:08Z)
• Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines [0.7156193707786874]
  This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in wind turbines.<n>The proposed method achieves an AUC-ROC of 0.947 and early fault detection up to 48 hours prior to failure.<n>This approach offers significant societal value by enabling predictive maintenance, reducing turbine failures, and enhancing operational efficiency in large-scale wind energy deployments.
  arXiv  Detail & Related papers  (2025-10-16T10:49:19Z)
• Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning [0.0]
  We present a novel generative deep transfer 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 detection in wind turbines with scarce data.
  arXiv  Detail & Related papers  (2025-04-24T16:14:04Z)
• Transfer learning applications for anomaly detection in wind turbines [0.0]
  Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. This study examines how cross-turbine transfer learning can be applied to autoencoder-based anomaly detection.
  arXiv  Detail & Related papers  (2024-04-03T18:48:45Z)
• Imbalanced Aircraft Data Anomaly Detection [103.01418862972564]
  Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task. We propose a Graphical Temporal Data Analysis framework. It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL)
  arXiv  Detail & Related papers  (2023-05-17T09:37:07Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.