A convolutional state-space framework for wind turbine fault diagnosis using hierarchical feature extraction and dynamic state modeling on SCADA system

Wind turbines are the backbone of renewable energy production but are often subject to mechanical failure that reduces efficiency and increases maintenance costs, as well as downtime. Traditional non-destructive testing and fault diagnosis approaches such as vibration analysis are inflexible and non-adaptive; on the other hand, deep learning-based methods lack interpretability of their practices. To tackle these issues, we present a fault diagnosis framework that combines 1D-CNN and Dynamic state-space model (DSSM) for 1D vibration signals from faulty turbine states. This novel modelling approach combines both local and temporal dependencies which helps improve interpretability and enables real-time fault detection. The efficacy of our approach is validated on a series of wind turbine fault conditions, showing competitive performance compared to classical machine learning models and deep learning models by achieving an average of 97% accuracy across all conditions, with high fault classification accuracy at different operational conditions as well.