Deep adversarial transfer neural network for fault diagnosis of wind turbine gearbox

Abstract

ABSTRACT Labeling the fault data is a time-consuming and expensive operation. Therefore, the monitoring data obtained from wind farms are rarely accurately labeled. The method of deep adversarial transfer neural network for diagnosis of gearbox in wind turbine was put forward, which used the auxiliary data set and solved the problem of large data distribution differences with the help of domain adversarial method to transfer the features learned by auxiliary data set to the data from wind turbines. The fault diagnosis model under the condition of unsupervised was established, which, to a certain extent, reduced the dependence of the deep learning model to the labeled data obtained from wind turbine. The effectiveness of proposed method was verified by using vibration data from bearing failure test at Case Western Reserve University and measured vibration data from the gearbox in wind turbine. The results showed that this method was effective in realizing the cross-domain transfer mission of the fault diagnosis model between similar domains and provided a new direction for constructing the data-driven fault diagnosis model.