A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.
Journal article
Shafique, R., Siddiqui, H., Rustam, F., Ullah, S., Siddique, Muhammad Abubakar, Lee, E., Ashraf, I. and Dudley-Mcevoy, S. (2021). A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis. Sensors. 21 (18). https://doi.org/s21186221
Authors | Shafique, R., Siddiqui, H., Rustam, F., Ullah, S., Siddique, Muhammad Abubakar, Lee, E., Ashraf, I. and Dudley-Mcevoy, S. |
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Abstract | Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%. |
Keywords | Machine Learning; deep convolution neural networks; machine learning; railway track inspection; logistic regression; Acoustics; Support Vector Machine; Neural Networks, Computer; railway track cracks detection; acoustic signals analysis; Algorithms; Humans |
Year | 2021 |
Journal | Sensors |
Journal citation | 21 (18) |
Publisher | MDPI |
ISSN | 1424-8220 |
Digital Object Identifier (DOI) | https://doi.org/s21186221 |
https://doi.org/10.3390/s21186221 | |
Funder/Client | MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program |
Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning | |
Publication dates | |
16 Oct 2021 | |
Online | 16 Sep 2021 |
Publication process dates | |
Deposited | 05 Nov 2021 |
Accepted | 11 Sep 2021 |
Publisher's version | License File Access Level Open |
Accepted author manuscript | License File Access Level Controlled |
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https://openresearch.lsbu.ac.uk/item/8y197
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