Fault Diagnosis for Wind Turbine Gearboxes by Using Deep Enhanced Fusion Network
Ziqiang Pu, Chuan Li, Shaohui Zhang, Yun Bai
Abstract
The gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are first applied to extract deep features of three-axial vibration signals, respectively. Second, a feature enhancement mapping is developed to minimize the intraclass distance of the deep features in the three-axial vibration. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy compared with other peer models.