A Fault Diagnosis Approach for Wind Turbine Gearbox Based on Ensemble Learning Model and Dung Beetle Optimization Algorithm

Abstract

The gearbox is one of the crucial components in wind turbines, and its performance degradation would give rise to out-of-order or even damage. To accurately identify the fault of the gearbox, a novel fault diagnosis approach based on the ensemble model and the dung beetle optimization algorithm is introduced. Firstly, an ensemble learning model with different activation functions is established. Secondly, the dung beetle optimization is applied to select the base models for improving the performance and generalization ability of the ensemble model. Finally, the proposed method is tested with a gearbox dataset. The experimental results show that the proposed approach achieves higher diagnostic accuracy on the gearbox dataset.