Title: A predictive model based on the LSTM technique for the maintenance of railway track system

Authors: Sharad Nigam; Divya Kumar

Addresses: Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, India ' Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, India

Abstract: Maintenance is a substantial process to sustain the operations of transportation systems. Railway is a major way of transportation, defect and failure in track side equipment and track itself may causes major loss of human lives. So, an effective maintenance technique is needed which saves the passenger lives and maximises the utilisation of railway track equipment, just before it fails, that cause casualty. There are various types of track defects to be inspected, but this paper only deals with surface defects, cross level, and DIP. By examining the track condition and expecting the RUL, the railway industry can schedule maintenance of railway tracks. In this research paper, we have tried to conduct a relative analysis of various machine learning algorithms by comparing their performance for estimation of future failure points of railway tracks to inspect the reliability of LSTM technique. Dataset is taken from the trusted source RAS Track geometry analytics (2015).

Keywords: predictive maintenance; K-nearest neighbour; KNN; machine learning; long short-term memory; LSTM; railway track; support vector machine; SVM; remaining useful life; RUL.

DOI: 10.1504/IJCSE.2025.143461

International Journal of Computational Science and Engineering, 2025 Vol.28 No.1, pp.10 - 20

Received: 12 Feb 2023
Accepted: 14 Jan 2024
Published online: 21 Dec 2024 *

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