Machine-Learning-Based Intelligent Mechanical Fault Detection and Diagnosis of Wind Turbines

Abstract

Wind power has gained wide popularity due to the increasingly serious energy and environmental crisis. However, the severe operational conditions often bring faults and failures in the wind turbines, which may significantly degrade the security and reliability of large-scale wind farms. In practice, accurate and efficient fault detection and diagnosis are crucial for safe and reliable system operation. This work develops an effective deep learning solution using a convolutional neural network to address the said problem. In addition, the linear discriminant criterion-based metric learning technique is adopted in the model training process of the proposed solution to improve the algorithmic robustness under noisy conditions. The proposed solution can efficiently extract the features of the mechanical faults. The proposed algorithmic solution is implemented and assessed through a range of experiments for different scenarios of faults. The numerical results demonstrated that the proposed solution can well detect and diagnose the multiple coexisting faults of the operating wind turbine gearbox.