Matching contrastive learning: An effective and intelligent method for wind turbine fault diagnosis with imbalanced SCADA data

Data-driven intelligent systems provide a possible solution to condition-based maintenance of wind turbines without experts' knowledge or mechanism models. However, the accuracy of fault diagnosis results is easily impaired by the data imbalance in real-world applications. In this paper, a novel method is proposed to address the mentioned problem. Specifically, spatial and temporal information of supervisory control and data acquisition (SCADA) data is extracted, and a contrastive learning strategy is developed to obtain the data representations correlated with the health conditions. Additionally, the bollards of the data distributions are determined in the representation space, and they are matched with the data centers of each health state. Under this circumstance, the unexpected effects of data imbalance on data representations are relieved, leading to effective decision boundaries regardless of the sample numbers. Then, a classifier is trained on the learned representations to recognize the faults in wind turbines. Compared with several baselines and state-of-the-art approaches, the impressive performance of the proposed method is demonstrated with simulated data and actual measurements from a utility-scale wind turbine. This research provides useful insights into the efficient development of wind energy with data-driven intelligent systems.

Automated summary

This paper proposes a novel method to address the problem of data imbalance in fault diagnosis of wind turbines. The method extracts spatial and temporal information from SCADA data and uses contrastive learning to obtain data representations correlated with the health conditions. The method achieves effective decision boundaries and impressive performance in recognizing faults in wind turbines.