Wind Turbine Fault Classification Based on Reconstruction Errors of SCADA Data

Wind turbine fault monitoring is crucial for reducing operational costs, yet remains challenging due to the scarcity of fault events and turbine-specific operational characteristics.This paper presents a novel two-stage approach combining turbine-specific anomaly detection with cross-turbine fault classification.We train individual autoencoders for each turbine to capture normal operational patterns and use their reconstruction errors as input for a shared InceptionTime classifier.This partitioning reduces the task of the classifier to classifying anomalies instead of general operational data while preserving turbine-specific particularities.Further, our approach can flexibly integrate existing methods for autoencoderbased fault detection and is, in principle, classifier agnostic.Using a comprehensive real-world dataset of 15 turbines with 4,899 repair events spanning at least four years of operation, we demonstrate that our approach consistently outperforms classification based directly on SCADA data.