Accurate fault detection in wind turbines is a key factor for improving their reliability and reducing maintenance cost. This report investigates probabilistic target variable modeling using a conditional Generative Adversarial Network (cGAN) in a Normal Behavior Modeling framework for fault detection on wind turbine drivetrains. The dataset used for the investigation is the open-source Supervisory Control and Data Acquisition (SCADA) dataset provided by Energias de Portugal (EDP). A cGAN and a traditional Recurrent Neural Network (the base model) are developed and trained to model the generator bearing temperature. The performance of the cGAN is compared to that of the base model, to highlight differences associated with the modeling approach. The cGAN’s output is a conditional distribution of the target temperature, given the operational state of the turbine. From this distribution, two methods of deriving a Health Indicator are investigated: (1) By reducing the distribution to a point estimate, and (2) by taking into account the whole distribution. From the base model, a third Health Indicator is derived.
For all three Health Indicators, anomalies are detected by performing a non-parametric hypothesis test designed to detect deviations from the empirical healthy data distribution. The findings indicate an enhanced temperature modeling performance of the cGAN on train, validation and test data. Especially the robustness appears to be significantly improved. While interpreting the outcome of applying the Health Indicators for fault detection remains challenging, the results suggest that the cGAN’s probabilistic modeling improves fault detection by reducing the number of false responses.