Diagnosis method of out-of-roundness of metro wheels based on vertical vibration acceleration of axle box

Firstly, convolutional neural network, deep belief network, support vector machine and support vector machine model with the full connection layer features of a one-dimensional convolutional neural network as input (1DCNN-SVM) were established respectively. Secondly, the effects of the above models on the classification of out-of-roundness of metro wheels were compared. The mapping relationship between the root mean square of the vertical acceleration of the axle box and the vehicle speed and the polygonal wear amplitude was constructed by surrogate models. Finally, the wheel polygonal wear amplitude was inversely solved by the intelligent optimization algorithm. The applicability of different surrogate models and intelligent optimization algorithms was compared in the identification of the wheel polydonal wear amplitude. The results show that the 1DCNN-SVM model achieves a classification rate of 99.82 % in four types of typical wheel out-of-roundness, such as normal, low-order polygons, high-order polygons and non-periodic non-roundness wheels. Compared with the other three classification methods, its generalization performance and reinforcement learning ability have obvious advantages. In terms of wheel polygonal wear amplitude identification, the method based on Kriging model(KSM) and particle swarm optimization algorithm(PSO) has better prediction stability and timeliness. © 2024 Central South University of Technology. All rights reserved.