The installation of wind turbines presents several challenges and difficulties for operators. Indeed, wind turbines often face harsh environmental conditions and complex operational requirements, leading to high costs for operation and maintenance (O&M). Unexpected component failures can result in unscheduled maintenance that can be expensive to achieve. To address these issues, we propose an approach for anomaly detection and explanation based on Supervisory Control and Data Acquisition (SCADA) data. In this study, we focus our attention on a critical component of the wind turbine system called the main bearing. The approach involves two main steps: training an autoencoder model to detect anomalies in main bearing observations and using a probabilistic graphical model to gain insights into the relationships between system components and the main causes of failures. The robustness and benefits of this method compared to other fault detection techniques are demonstrated through numerical experiments.
Article ID: 2023L31
Publisher: Canadian Artificial Intelligence Association