Affiliations: College of Transportation, Jilin University, Changchun, China
Correspondence:
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Corresponding author. Zhang Yirui, College of Transportation, Jilin University, Changchun, China.
E-mail: [email protected].
Abstract: Track irregularity is the main source of vibration excitation of vehicle-track weighing system, which has an important impact on the safety, stability and comfort of train operation, and it is also the main factor limiting the speed of train operation. Higher requirements are put forward for track smoothness with the rapid development of China’s railways. Therefore, it has a great theoretical and practical significance to study the relationship between track irregularity and random vibration of vehicle-track cooker-in system and the evaluation method of track smoothness. In this paper, a model construction method is proposed based on machine learning fuzzy logic control and neural network algorithm to analyze the high frequency dynamic characteristics of ballastless track wheel-rail vehicle system. Firstly, a numerical analysis model of high frequency dynamic characteristics of ballastless track wheel-rail vehicle system is established by using linear discrete elastic-yellow damper element connection model; Secondly, the Rough Set Block Neural Network is introduced to optimize the dynamic characteristic analysis model, and the intelligent model of model optimization analysis is established. Finally, the validity of the proposed algorithm is verified by simulation experiments of practical examples.
Keywords: Ballastless track, wheel-rail vehicle, high frequency vibration, dynamic characteristics, fuzzy logic control
