Fault Diagnosis of Wind Motor based on Convolutional Neural Network

This paper studies the fault diagnosis of wind motors, which is an important way to improve the safety and reliability of wind motors. It is non-trivial to extract the fault features from the original vibration signals by the traditional methods. We propose a novel method to improve the fault diagnosis performances of wind motors. First, the Wigner-Ville distribution method is used to generate the time-frequency images of the vibration signals in different speed ranges of the motor, which is helpful for fault features extraction. Then, we use the convolutional neural network, an important tool in the field of deep learning, to extract the fault features from the time-frequency images. Finally, simulation results based on the measurement data of an actual wind motor are provided to demonstrate the effectiveness of the proposed method.