Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 10, Issue 1


Published
on

March 17, 2025


Pages


DOI

Article

Fault prediction and maintenance of urban rail transit power supply system based on big data

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Authors

Wenfei Zhao Affiliation:
Urban Rail Transit and Information Technology College, Liuzhou Railway Vocational Technical College, Liuzhou, Guangxi, 545616, China.
and Jiao Deng Affiliation:
Guangxi Technician College of Machinery &Electricity, Liuzhou, Guangxi, 545006, China.


Abstract

In order to guarantee the normal operation of urban rail transit system, the research constructs the fault prediction system of urban rail transit power supply system, analyzes the equipment failure rate under each weather condition, and constructs the Monte Carlo-based traction power supply fault simulation model. After the fault ranging of the traction power supply system, the fault prediction reasoning is carried out. The fault prediction method of this paper is used in the prediction of contact network wear, and its prediction effect on the degree of contact network wear is initially explored. The prediction method of this paper is compared with other methods, and three exogenous faults are predicted to verify the performance of the fault prediction method of this paper. Finally, the fault prediction system of this paper is utilized to diagnose and predict the reactor insulation faults, and the maintenance decision-making method is proposed. The prediction method in this paper achieves the best prediction results in contact network abrasion prediction, contact network lightning trip fault prediction, floating object infestation contact network fault prediction and haze fouling flash fault prediction. In reactor insulation fault prediction, the method in this paper can accurately determine the reactor front-end grounding and accurately predict the degree of grounding insulation degradation.


Keywords

Monte Carlo simulation, Traction transformer, Fault ranging, Fault prediction, 68T01


Citation

Zhao, W. & Deng, J. (2025). Fault prediction and maintenance of urban rail transit power supply system based on big data. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-0225

Published by: Engineering Journals

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