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Railway Defect Detection Based on Track Geometry Using Supervised and Unsupervised Machine Learning by Jessada Sresakoolchai; Sakdirat Kaewunruen is a Engineering article available to read on EtoBox.

What is Railway Defect Detection Based on Track Geometry Using Supervised and Unsupervised Machine Learning about?

Track quality affects passenger comfort and safety. To maintain the quality of the track, track geometry and track component defects are inspected routinely. Track geometry is inspected using a track geometry car (TGC). Measured values are stored in the machine and processed to evaluate the track quality. However, track component defects require more effort to inspect. Track component defects can be inspected manually which is time- and workload-consuming or using sensors installed at additional cost. This study presents an approach using track geometry obtained by a TGC to detect track component defects, namely, rail, switch and crossing, fastener and rail joint defects. Detection models are developed using several supervised machine learnings. The relationships between track component defects are analysed to gain insights using unsupervised machine learnings. From the study, the best model for detecting track component defects using track geometry is a deep neural network with an accuracy of 94.31% followed by a convolutional neural network with an accuracy of 93.77%. For the exploration of insights, k-means clustering is used to cluster the track components defects, and associat

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Author

Jessada Sresakoolchai; Sakdirat Kaewunruen

Publisher

SAGE Publications

Published

2022

Language

EN

Field

Engineering (Physical Sciences)