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

•A time series data-driven approach to the detection of anomalies and early faults.•An end-to-end one-class classification algorithm with no need of prior knowledge.•A novel loss function to learn Shapelets and SVDD decision boundary jointly.•A multi-scale hyper-parameter setting strategy to avoid tuning.