An Interpretable Machine Learning Ensemble Approach for Fault Detection in Floating Offshore Wind Turbines

This paper introduces an interpretable ensemble machine learning approach specifically designed for fault detection in floating offshore wind turbines. The methodology integrates advanced statistical features extracted from residual signals with complementary machine learning models, enhancing the identification of subtle fault-induced deviations typical in offshore environments. Validated using a realistic offshore wind farm simulation benchmark, the proposed method demonstrated clear advantages over traditional threshold-based techniques and single-model approaches. The practical interpretability of the method is demonstrated through analysis of feature relevance, aiding effective fault diagnosis. Although tested primarily on specific sensor faults, the modular nature of the methodology supports its generalisation and highlights its potential suitability for broader fault detection scenarios and real-time applications. We use at-least-one fusion and healthy-data calibration to meet a target false-alarm rate reproducibly.