Deep Unsupervised Transfer Learning for Health Status Prediction of a Fleet of Wind Turbines with Unbalanced Data



Published Nov 24, 2021
Dandan Peng Chenyu Liu Wim Desmet Konstantinos Gryllias


The condition monitoring and health status prediction of a fleet of wind turbines are essential for the safety of wind turbines. At present, the Supervisory Control And Data Acquisition (SCADA) system has been widely used in wind turbines, which can monitor and collect various physical information and sensor information of wind turbines in real-time. Due to the fact that the amount of data obtained by SCADA systems is extremely large, developing an intelligent decision-making system based on deep learning is a very valuable research. Therefore, this paper is committed to exploring a health status prediction algorithm of wind turbines based on deep learning and SCADA systems. However, yet in actual industrial applications, it is very time-consuming and expensive to obtain a large amount of labeled data. In addition, as failures rarely occur, there is a serious sample imbalance problem in the datasets. More importantly, due to the difference in working environment and physical parameter setting, there are significant differences in the feature distribution of different wind turbines data, which leads to a significant drop in the performance of the deep learning model on unknown wind turbines.

Therefore, an unsupervised transfer learning algorithm based on Generative Adversarial Networks for wind turbine health status prediction (WT-GAN) is proposed. WT-GAN can not only remove the domain shift between wind turbines, but also it is an unsupervised learning method. This means that only the unlabeled data for the target domain is required, which solves the problem of labeling data. In order to evaluate the effectiveness of WT-GAN on the condition monitoring of a fleet of wind turbines, this method is applied to one dataset about blade icing detection of wind turbines. The experimental results prove that the proposed method can predict the health status of the wind turbine well. In addition, it can significantly reduce the domain shift among different wind turbines, thereby achieving excellent performance on unknown wind turbines.

How to Cite

Peng, D., Liu, C., Desmet, W., & Gryllias, K. (2021). Deep Unsupervised Transfer Learning for Health Status Prediction of a Fleet of Wind Turbines with Unbalanced Data. Annual Conference of the PHM Society, 13(1).
Abstract 632 | PDF Downloads 419



condition monitoring, wind turbines, unsupervised transfer learning

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