Intelligent Anomaly Detection for Drivetrain Systems in Wind Turbines

The safety and reliability of the drivetrain system in offshore wind turbines are crucial for their effective operation. Detecting anomalous behaviour within the drivetrain and providing reliable prognostic information can significantly reduce the risk of severe failures. Ensuring the reliability and safety of intelligent models is of paramount importance in the AI-driven, data-centric era. To address this challenge, this paper presents an intelligent anomaly detection model capable of issuing alerts prior to abnormal shutdowns, thereby ensuring system safety. A physics-informed probabilistic neural network was developed, integrating physical insights into the neural framework to manage prediction uncertainty and enhance the safety and reliability of failure alarms generated by the intelligent model. Overall, the proposed method offers a more reliable prognostic framework to enhance the safety and stability of wind turbines, including offshore installations, during operation while reducing costs.

Keywords: Reliable prognostics, Anomaly detection, Physics-informed neural network, Offshore wind turbine, Drivetrain, SCADA data.