Magnetic Signature-Based Model Using Machine Learning for Electrical and Mechanical Faults Classification of Wind Turbine Drive Trains

Signal processing and fault indicators analysis are essential for efficient fault detection, classification, and diagnosis of wind turbines. Accordingly, existing works proposed the installation of multiple intrusive sensors (e.g., current, voltage, and accelerometer) for data collection in order to detect and classify the faults in wind turbine drive trains (WTDT). However, these sensors are scattered on the drive train and have a limited local reach on its components making it technically difficult to install. Therefore, signals from these sensors are not able to detect multi parameter phenomena such as coupling of the mechanical and electrical components of the drive train which contains essential fault information. This work proposes the use of magnetic signatures as fault condition indicators of the complete drive train due to the ability of contactless measurement of this signal without opening the main components of the drive train. This is achieved by performing non-destructive magnetic modeling and analysis of the entire drive train. The air gap magnetic flux density of the wind generator is demonstrated as a good fault condition indicator for different common faults occurring on the gearbox, bearings, and the generator. The proposed model is validated using a supervised machine learning classification algorithm in a way to distinguish between electrical and mechanical faults.