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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

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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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