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A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine).

Jin-Han Lee1, Jun-Hee Lee2, Kwang-Su Yun1

  • 1Busan Transportation Corporation, Busan 47353, Republic of Korea.

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|October 28, 2023
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Summary
This summary is machine-generated.

This study introduces a new method for predicting railway wheel defects using sensor data. Machine learning accurately classifies wheel conditions, improving railway safety and operations.

Keywords:
machine learning algorithmrecognizing condition algorithmtirewheel

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Area of Science:

  • Railway Engineering
  • Machine Learning Applications
  • Predictive Maintenance

Background:

  • Current railway wheel management relies on post-event inspections after issues arise.
  • This reactive approach poses risks to operational safety and efficiency.
  • There is a need for proactive methods to detect and predict wheel abnormalities.

Purpose of the Study:

  • To develop an advanced method for the early prediction of railway wheel abnormalities.
  • To enhance the performance of machine learning algorithms for wheel condition classification.
  • To improve the safety and reliability of railway operations through predictive maintenance.

Main Methods:

  • Collected real-time operational data from sensors on railway vehicles (Busan Metro Line 4).
  • Analyzed key factors and performed data distribution and correlation analyses to identify critical parameters for classification.
  • Applied machine learning algorithms, including Support Vector Machine (SVM) with Linear and RBF Kernels, and Random Forest, using acceleration data.

Main Results:

  • Identified the z-axis of acceleration as a significant factor for classifying wheel conditions.
  • Achieved high accuracy in classifying railway wheels as in-service or defective using machine learning models.
  • The SVM (Linear Kernel) model demonstrated the highest recognition rate at 98.70%.

Conclusions:

  • The proposed method effectively predicts railway wheel abnormalities using acceleration data.
  • Machine learning, particularly SVM (Linear Kernel), offers a highly accurate solution for real-time wheel condition monitoring.
  • Implementing this predictive approach can significantly enhance railway safety and operational efficiency.