A class-imbalance-aware domain adaptation framework for fault diagnosis of wind turbine drivetrains under different environmental conditions

This paper proposes a new fault diagnosis model for wind turbine drivetrains addressing time-varying environmental conditions and the limited availability of fault-related data. Wind turbines continuously work under variable conditions, making it difficult to generalize a baseline-trained machine learning model to these conditions with different domain distributions. On the other hand, limited labeled data are usually available for failure …