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Fault analysis of railway track superstructure using time-frequency method from vibrational data

Authors

  • Shahzor Memon

    Department of Electrical Engineering, Hamdard University, Karachi, Pakistan
    Author

  • Imtiaz Hussain

    Department of Computer Science, Iqra University, Karachi, Pakistan
    Author

  • Lubna Moin

    Department of Electrical Engineering, PNEC NUST, Karachi, Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.0032

Keywords:

Track fault Analysis , Short-time Fourier Transform, Vibrational Data, Time-Frequency

Abstract

Deficiencies in railway track superstructure can result in accidents, leading to human casualties and financial losses. Axle loads and speed, insufficient rail hardness, environmental factors, wear, and corrosion lead to corrugation, ballast issues, and other track surface faults that lead to accidents. A manual examination is prone to mistakes, is time-consuming, and can be biased. Automated inspection offers a rapid, reliable, and impartial choice. To obtain better performance, the research focuses on the time-frequency analysis of vibrational data using the Fast Fourier Transform and Short-time Fourier transform to identify the fault signatures. Car body acceleration simulated by a coupled 2D train track Bridge interaction model with 6 DOF of train vehicle with 5 layered track model. The results indicate a change in the elevation of the vehicle's profile owing to the ballast module deformation and the welds at the ends of the bogies. In turn, the corrugation and ballast irregularities affect the vertical acceleration of the car body, by examining the time-domain responses and frequency-domain spectrograms (via Short-Time Fourier Transform (STFT) analysis). The results indicate that track faults such as corrugation introduce dominant high-frequency components (typically below the range of 50 Hz) with moderate amplitude. At the same time, ballast issues manifest as low-frequency components (below 30 Hz) with increased amplitude escalation. The safety and operational efficiency of railroads could be improved by further research that combines these time-frequency characteristics with machine learning algorithms to increase fault detection accuracy and create monitoring systems for more effective proactive maintenance.

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References

R. Shafique et al., "A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis”, Sensors, 2021.

Wikipedia contributors, "Pakistan Railways”, Wikipedia, The Free Encyclopedia, Nov. 9, 2023. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Pakistan_Railways&oldid=1184270149

D. Cantero, "TTB-2D: Train–Track–Bridge interaction simulation tool for Matlab”, SoftwareX, 2022.

D. Cantero, T. Arvidsson, E. O'Brien, and R. Karoumi, "Train–track–bridge modeling and review of parameters”, Struct. Infrastruct. Eng., vol. 12, no. 9, pp. 1051–1064, 2016. [Online]. Available: http://dx.doi.org/10.1080/15732479.2015.1076854

H. Tsunashima and R. Hirose, "Condition monitoring of railway track from car-body vibration using time-frequency analysis”, Veh. Syst. Dyn., vol. 60, pp. 1170–1187, 2022.

H. Tsunashima and M. Takikawa, "Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network”, in Recent Advances in Wavelet Transforms and Their Applications, IntechOpen, Nov. 23, 2022. doi: 10.5772/intechopen.102672

Mhmood, Z.S., Nasret, A.N., Noori, A.B., Mohammed, A.B. (2023). Neural network-directed detection and localization of faults in railway track circuits: An application of Dempster-Shafer theory. Ingénierie des Systèmes d’Information, Vol. 28, No. 3, pp. 583-593. https://doi.org/10.18280/isi.280306

Castillo-Mingorance, J.M.; Sol-Sánchez, M.; Moreno-Navarro, F.; Rubio-Gámez, and M.C. A Critical Review of Sensors for the Continuous Monitoring of Smart and Sustainable Railway Infrastructures. Sustainability 2020, 12, 9428. https://doi.org/10.3390/su12229428

C. Zhao, Y. Wang, Y. Zhao, and D. Shi, "Identification of Multiple Defects from Rail Vibration Signals Based on Fast Kurtogram”, J. Transp. Eng., vol. 149, no. 6, 2023, Art. no. 04023035. doi: 10.1061/JTEPBS.TEENG-7783

N. Ahsan et al., "A critical review of sustainable rail technologies based on environmental, economic, social, and technical perspectives to achieve net zero emissions”, Renew. Sustain. Energy Rev., vol. 185, 2023, Art. no. 113621.

S. Khan et al., "Comprehensive Risk Assessment of Pakistan Railway Network: A Semi-Quantitative Risk Matrix Approach”, Heliyon, 2024.

S. Yan, L. Huang, P. Xu, and M. Wan, "Real-Time Rail Fault Diagnosis Based on Vibration Signal Analysis and Second-Order Sinusoidal Model”, IEEE Sens. J., vol. 22, no. 4, pp. 3382–3396, 2022. doi: 10.1109/JSEN.2021.3139025

J. Zhang, Q. Gao, S. J. Tan, and W. X. Zhong, "A precise integration method for solving coupled vehicle–track dynamics with nonlinear wheel-rail contact”, J. Sound Vib., vol. 331, pp. 4763–4773, 2012.

M. El Moueddeb, F. Louf, P.-A. Boucard, F. Dadié, G. Saussine, and D. Sorrentino, "An Efficient Numerical Model to Predict the Mechanical Response of a Railway Track in the Low-Frequency Range”, Vibrations, 2022.

M. Naeimi, J. A. Zakeri, M. Esmaeili, and M. Shadfar, "Influence of uneven rail irregularities on the dynamic response of the railway track using a three-dimensional model of the vehicle–track system”, Veh. Syst. Dyn., vol. 53, no. 1, pp. 88–111, 2015. [Online]. Available: http://dx.doi.org/10.1080/00423114.2014.998243

D. Łuczak, "Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network”, Electronics, vol. 13, no. 12, Art. no. 2411, 2024. doi: 10.3390/electronics13122411

A. Shah, B. S. Chowdhry, T. D. Memon, I. H. Kalwar, and A. Ware, "Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform”, Ann. Emerg. Technol. Comput. (AETiC), vol. 4, no. 2, 2020. [Online]. Available: https://ssrn.com/abstract=3760258

Y. Bai, W. Cheng, W. Wen, and Y. Liu, "Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis”, Shock Vib., 2023, Art. no. 9878228. doi: 10.1155/2023/9878228

M. Hussain, T. D. Memon, I. Hussain, and Z. A. Memo, "Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors”, CMES-Comput. Model. Eng. Sci., 2022.

A. Malekjafarian, C. A. Sarrabezolles, M. A. Khan, and F. Golpayegani, "A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train”, Sensors (Basel), vol. 23, no. 17, Art. no. 7568, Aug. 31, 2023. doi: 10.3390/s23177568

S. L. Grassie, "Rail corrugation”, in Wheel–Rail Interface Handbook, Woodhead Publishing, 2009, pp. 349–376.

Published

2025-07-01

Issue

Vol. 44 No. 3 (2025): July Issue

Section

Articles

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Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

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How to Cite

Fault analysis of railway track superstructure using time-frequency method from vibrational data. (2025). Mehran University Research Journal of Engineering and Technology, 44(3), 01-09. https://doi.org/10.22581/muet1982.0032

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