Fault Diagnosis of Wind Turbine Bearings Based on CNN and SSA-ELM

Purpose As a critical component of the wind turbine drive train, the bearings are easy to fail under the complex environment of variable working conditions and loads in long-term operation. So it is essential to carry out a study targeting at fault diagnosis on it to improve the safety and reliability of the whole wind turbine operating. Methods This paper presents a kind of bearing fault diagnosis method for wind turbines based on convolutional neural network (CNN) and sparrow search algorithm (SSA) optimized extreme learning machine (ELM). First, the wavelet time-frequency diagram (WTD) is constructed by using the continuous wavelet transform (CWT) to the original vibrational signal of the wind turbine bearing. Then, the WTD is input into deep learning CNN for extracting features. Finally, the SSA-ELM classifier is constructed by searching the optimal parameters of ELM with SSA, and the extracted features are put into SSA-ELM to identify different fault types. Results The proposed CWT-CNN-SSA- ELM method is experimentally validated by two bearing datasets and compared with other methods. The result shows that the method has better diagnosis capability. Conclusion In this paper, a wind turbine bearing fault diagnosis method based on CNN and SSA-ELM is proposed. The approach is able to well extract fault features and classify and identify the bearing data under variable working conditions and time-varying speed with good generalization ability.