Small samples noise prediction of train electric traction system fan based on a multiple regression-fuzzy neural network

Abstract

The fan noise in the train electric traction system contributes to the main noise of the train, affecting the comfort of the passengers. The prediction of fan noise can serve as a foundation for the design and fault diagnosis of the fan in the train electric traction system as well as noise reduction. This paper proposes a prediction model based on multiple regression analysis and a double-layer fuzzy neural network (MRA-2LFNN). After the multiple regression analysis of the train electric traction system fan noise is performed to determine the prediction input, a fuzzy neural network is used to predict the fan noise. Afterward, further analysis of noise influencing factors is presented based on the experimental results, and the possibility of the fan optimal design is discussed. This method can make accurate predictions using a small sample of fan noise data, reducing the need for data samples and lowering costs. The experimental results show that the proposed method can achieve better prediction results using the small sample dataset by training and verifying the actual train electric traction system fan noise data, and the average prediction accuracy rate is 94.15%, After discussion, when the number of fan blades is 16, the train electric traction system exhibits improved noise performance.