Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis

Abstract

Fault diagnosis of wind turbines suffers from less training data and noises. A method based on wide deep convolutional neural network with resampling and principal component analysis was presented for the diagnosis of mechanical faults (that is the main fault component of wind turbines). The method adopted a variety of signal preprocessing methods such as resampling wavelet threshold denoising and principal component analysis to increase the information density and ensure the integrity of the information. After being trained with small amount of data, the network which has a powerful feature extraction capability could extract the fault signal in the time domain which will be further used for fault diagnosis. Experimental results were verified based on the real wind turbine data, demonstrating the effectiveness of this method.

Keywords

• wind turbine
• wide deep convolutional neural network
• resampling
• wavelet threshold denoising
• principal component analysis