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Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method.

Xiao-Zhou Liu1,2, Chi Xu3,4, Yi-Qing Ni5,6

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Detecting wheel tread defects on high-speed trains is crucial for safety. A new fiber Bragg grating system effectively identifies minor defects using advanced signal processing, preventing severe damage.

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Area of Science:

  • Railway Engineering
  • Condition Monitoring
  • Non-destructive Testing

Background:

  • Out-of-roundness (OOR) defects on high-speed train wheels can cause significant damage to vehicles and tracks.
  • Timely detection and re-profiling of defective wheels are essential for operational safety and infrastructure integrity.

Purpose of the Study:

  • To develop and validate a wayside system for online detection of wheel tread defects.
  • To implement advanced signal processing for identifying subtle defects that are difficult to detect with conventional methods.

Main Methods:

  • A fiber Bragg grating (FBG)-based wayside monitoring system was deployed to collect rail strain data.
  • A Bayesian blind source separation (BSS) method was used to extract defect-sensitive features from the rail response signals.
  • Chauvenet's criterion was applied to identify anomalies indicative of wheel defects.

Main Results:

  • The system successfully detected all wheel tread defects during a blind test.
  • Detected defects correlated well with offline measurements of wheel radius deviation.
  • Minor defects with a radius deviation as small as 0.06 mm were accurately identified.

Conclusions:

  • The proposed FBG-based system with BSS signal processing is effective for online detection of wheel tread defects.
  • This technology enables early identification of minor defects, preventing potential damage and ensuring train safety.
  • The system offers a reliable solution for proactive maintenance of high-speed train wheelsets.