Transfer Deep Learning Network for Rolling Bearing Fault Diagnosis of Wind Turbines

Abstract

Fault diagnosis of rolling bearings has become a critical measure to ensure the security, efficiency, and availability of wind turbine systems. In this work, an approach called a transfer deep learning network is reported to resolve the drawbacks of existing rolling bearing fault algorithms on the basis of deep learning, such as large training parameters, long training time, and insufficient training samples. The reported approach mainly consists of two steps: feature transfer using transfer component analysis and network transfer using the pre-trained convolutional neural network. One dataset of rolling bearings with four fault types is gathered to estimate the classification performance, and the peer model is employed for a comparison study. The derived research results show that the reported algorithm based on the feature transfer and the network transfer can yield outstanding results in the rolling bearing fault diagnosis.