Fault online simulation of train braking system based on AMESim and LabVIEW

Jingxian Ding; Jianyong Zuo

doi:10.1117/12.2660842

16 December 2022 Fault online simulation of train braking system based on AMESim and LabVIEW

Jingxian Ding, Jianyong Zuo

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250024 (2022) https://doi.org/10.1117/12.2660842
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Aiming at the lack of fault data and online simulation difficulties in the intelligent operation and maintenance of the braking system of trains, the braking system of high-speed trains was taken as the research object in this paper, and the fault online simulation method of braking system was studied. First, by analyzing the structural composition and functional principle of the braking system, a simulation model of the braking system was established by using AMESim software. Secondly, the fault injection methods for different faults of the braking system in the AMESim simulation model were proposed. Then, in order to achieve real-time fault injection, LabVIEW and AMESim were jointly simulated to establish a multi-domain digital model of the braking system. Finally, some typical faults of the braking system were injected into the multi-domain digital model for online simulation and compared with the theoretical analysis results. The results show that the outputs of fault online simulation are in good agreement with the theoretical analysis ones. The method proposed in this paper provides an effective way to obtain online fault simulation data for intelligent operation and maintenance of the braking system of trains.

Citation Download Citation

Jingxian Ding and Jianyong Zuo "Fault online simulation of train braking system based on AMESim and LabVIEW", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250024 (16 December 2022); https://doi.org/10.1117/12.2660842

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