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

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).