Anomaly Score-Based Cross-Machine Wind Turbine Component Diagnosis: A Case Study and Benchmark

Reliable wind turbine component diagnosis requires cross-machine knowledge transfer since verified fault cases are rare. However, coarse fault interval annotations and turbine-specific operating behavior make this transfer difficult. This paper introduces a benchmark framework for cross-machine wind turbine component diagnosis on a new industrial dataset derived from Supervisory Control and Data Acquisition (SCADA) and vibration-derived kinematics data. The dataset is publicly available upon request and contains anomaly score-based embeddings. These embeddings are feature vectors whose entries quantify how strongly signals or component-related features deviate from learned normal behavior. The benchmark compares classical machine learning models, deep learning models, adversarial domain adaptation variants, and a simple baseline. Hyperparameter selection is performed without target labels using source classification loss, target prediction entropy, or Soft Neighborhood Density (SND). Our experiments show that, in the SCADA validation stage, source classification loss achieves the highest mean case diagnosis score among the evaluated objectives. In our evaluation, simple baselines remain highly competitive. The SCADA component score baseline achieves the highest test case diagnosis accuracy, while an ensemble of one-class support vector machines achieves the highest kinematics test case diagnosis accuracy. These findings indicate that anomaly score-based embeddings provide a useful representation for real-world component diagnosis, but that the small number of verified cases, coarse fault interval annotations, and partial label space mismatch remain major obstacles for reliable cross-machine adaptation.