Railway track fault detection using optimised convolution neural network

R. Chitra, A.M. Anusha Bamini, D. Brindha, T.M. Chenthil Jegan and S. Stewart Kirubakaran

International Journal of Reliability and Safety, 2024, vol. 18, issue 2, 163-186

Abstract: Railway accidents are an under-scrutinised cause of death in India. Train accidents are caused by various consequences of collisions, derailments, signal errors and so on. Furthermore, when train derailments become disastrous, they can have tremendous repercussions. It is practically difficult to identify the cause of the derailment efficiently within a limited period. In recent years, we have been making progress in reducing derailments, but even if not deadly, identifying faulty tracks can waste a lot of time and money. And doing this error-free is a pressing matter, as tracks always experience wear and tear with more usage. Here is where neural networks can pitch in their solution. We can train a model to look at train tracks and identify any issues. This paper goes into the methodology of achieving this and optimising a neural network to predict problems in the track with the best possible accuracy that images can provide. The objective of this paper is to identify, develop and optimise neural networks to detect faulty tracks. In this work, a good Convolution Neural Network model is developed to identify the crack in the railway track. The developed model produced 95.54% accuracy in fault classification.

Keywords: activation function; track; neural network; optimisation; VGG. (search for similar items in EconPapers)
Date: 2024
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