A one-class Shapelet dictionary learning method for wind turbine bearing anomaly detection

Detecting main shaft bearing anomaly is crucial to ensure the safe operation of wind turbines. However, existing anomaly detection methods have a limitation that anomaly samples are required for hyper-parameters tuning. Because of the scarcity of anomaly samples in the real-world scenarios, it is difficult to implement such existing methods in real-world applications. This paper proposes an end-to-end anomaly detection algorithm named one-class Shapelet dictionary learning. Firstly, the loss function of Shapelet dictionary learning is modified by integrating a soft-boundary term, so that the features and decision boundary can be learnt jointly. Then, a hyper-parameter setting strategy is introduced, so that anomaly samples are not needed in the training stage. The proposed method is validated through a case study collected from a real-world wind power farm. Results shown that the proposed method has a better F1 score than all baselines while anomaly samples are totally banded in the training stage.