Coupler Life Prediction Based on LSTM

Amber Liu (), Zilin Cai () and Daqing Gong ()
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Amber Liu: University of Toronto
Zilin Cai: Beijing Jiaotong University
Daqing Gong: Beijing Jiaotong University

A chapter in LISS 2024, 2025, pp 1056-1064 from Springer

Abstract: Abstract As a key component connecting railway freight cars, the coupler plays a crucial role in the safety and stability of railway freight cars. However, currently, most enterprises still rely too heavily on empiricism in terms of the service status and remaining life judgment of couplers, lacking systematic and scientifically analyzed life prediction. Therefore, studying the service life characteristics of couplers and conducting reasonable life prediction analysis is of great significance for maintaining railway equipment, assisting railway departments and related enterprises in decision-making. This article focuses on the multidimensional, random, and temporal characteristics of train coupler data, and uses the LSTM model to predict and analyze the life of the coupler. The results indicate that the LSTM based coupler life predictive model has high accuracy and reliability, and can effectively cope with the complex characteristics of time series data. This study provides a feasible prediction method for railway transportation management, which is expected to provide strong support for the rational allocation of couplers and equipment maintenance in practical applications.

Keywords: LSTM model; coupler; life prediction; time series (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_80

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DOI: 10.1007/978-981-96-9697-0_80

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