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A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train.

Abdollah Malekjafarian1, Chalres-Antoine Sarrabezolles2, Muhammad Arslan Khan1

  • 1Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland.

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|September 9, 2023
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Summary
This summary is machine-generated.

This study introduces a new railway track monitoring method using train acceleration data to detect stiffness loss. An Artificial Neural Network (ANN) effectively identifies track damage by analyzing acceleration signal energies.

Keywords:
ANNSHMaccelerationdrive-by monitoringin-service train measurementsmachine learningrailway infrastructure monitoringtrack damage detection

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Area of Science:

  • Civil Engineering
  • Railway Engineering
  • Structural Health Monitoring

Background:

  • Railway track degradation, particularly loss of stiffness in sub-layers, can compromise safety and operational efficiency.
  • Traditional track inspection methods are often labor-intensive, costly, and may not detect subsurface issues effectively.
  • Developing non-disruptive monitoring techniques is crucial for proactive maintenance and ensuring railway infrastructure integrity.

Purpose of the Study:

  • To propose and validate a novel approach for detecting stiffness loss in railway track sub-layers using in-service train measurements.
  • To develop an Artificial Neural Network (ANN) model capable of identifying track damage based on acceleration response energies.
  • To introduce a Damage Indicator (DI) for visualizing and quantifying track degradation.

Main Methods:

  • Utilized acceleration responses from an in-service train, processed into energy values per 15m track slice.
  • Developed and trained an ANN model using simulated and real-world train acceleration data for healthy track conditions.
  • Simulated track damage by reducing sub-ballast layer stiffness, representing hanging sleepers.
  • Introduced a DI based on prediction errors between simulated and predicted energies to assess damage levels.
  • Conducted a sensitivity analysis on the DI's performance considering signal noise, slice size, and multiple damage locations.

Main Results:

  • The ANN model successfully learned healthy track conditions from acceleration signal energies.
  • The proposed Damage Indicator (DI) effectively visualized and differentiated various levels of stiffness loss in the track sub-layers.
  • Sensitivity analysis indicated the DI's robustness against moderate signal noise and variations in slice size.
  • The method demonstrated potential in identifying multiple damaged locations, though performance varied.

Conclusions:

  • The developed ANN-based approach offers a promising, non-disruptive method for monitoring railway track sub-layer stiffness.
  • The proposed Damage Indicator provides a valuable tool for visualizing and assessing track degradation, aiding proactive maintenance.
  • Further research is recommended to refine the DI for complex scenarios, including varying train speeds and diverse damage types.