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
AuthorsShafique, R., Siddiqui, H., Rustam, F., Ullah, S., Siddique, Muhammad Abubakar, Lee, E., Ashraf, I. and Dudley-Mcevoy, S.
AbstractRegular 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%.
KeywordsMachine 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
Year2021
JournalSensors
Journal citation21 (18)
PublisherMDPI
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/s21186221
https://doi.org/10.3390/s21186221
Funder/ClientMSIT(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
Print16 Oct 2021
Online16 Sep 2021
Publication process dates
Deposited05 Nov 2021
Accepted11 Sep 2021
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Experimental Realization of a Single-Phase Five Level Inverter for PV Applications
Loukriz, A, Dudley, S, Quinlan, T and Walker, S (2016). Experimental Realization of a Single-Phase Five Level Inverter for PV Applications. IEEE Workshop on Control and Modeling for Power Electronics (COMPEL) 2016. Trondheim, Norway 27 - 30 Jun 2016 Institute of Electrical and Electronics Engineers (IEEE).
Huygens Principle based UWB Microwave Imaging Method for Skin Cancer Detection
Ghavami, N, Tiberi, G, Ghavami, M, Dudley, S and Lane, ME (2016). Huygens Principle based UWB Microwave Imaging Method for Skin Cancer Detection. 10th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing. Prague, Czech Republic 20 - 22 Jul 2016 Institute of Electrical and Electronics Engineers (IEEE).
Iterated Local Search Algorithm for Clustering Wireless Sensor Networks.
Dudley, S, Oladimeji, MO and Turkey, M (2016). Iterated Local Search Algorithm for Clustering Wireless Sensor Networks. 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada 24 - 29 Jul 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC.2016.7744200
Development of a Wall Climbing Robotic Ground Penetrating Radar System for Inspection of Vertical Concrete Structures
Sattar, TP, Howlader, MOF and Dudley, S (2016). Development of a Wall Climbing Robotic Ground Penetrating Radar System for Inspection of Vertical Concrete Structures. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering. 10 (8), pp. 1346-1352.
A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network
Oladimeji, MO, Turkey, M and Dudley, S (2016). A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network. EvoApplications Evostar 2016. Porto, Portugal 30 Mar - 01 Apr 2016 https://doi.org/10.1007/978-3-319-31204-0_17
A novel single-phase thirteen level inverter for photovoltaic application
Loukriz, A, Dudley, S, Messalti, S, Quinlan, T, Loukriz, A and Walker, S (2016). A novel single-phase thirteen level inverter for photovoltaic application. 8th International Conference on Modelling, Identification and Control (ICMIC-2016). Algiers, Algeria- November 15-17, 2016 15 - 17 Nov 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 532-537 https://doi.org/10.1109/ICMIC.2016.7804170
A user-centric system architecture for residential energy consumption reduction
Vastardis, N, Adjrad, M, Buchanan, K, Liao, Z, Koch, C, Russo, R, Yang, K, Ghavami, M, Anderson, B and Dudley, S (2014). A user-centric system architecture for residential energy consumption reduction. IEEE Online Conference on Green Communications. Online 12 - 14 Nov 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-7 https://doi.org/10.1109/OnlineGreenCom.2014.7114423