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Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing

Xiaoying Yu1, Hongsheng Su1, Zeyuan Fan2

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A novel Fast Simulated Annealing-Gaussian Process Regression (FSA-GPR) algorithm accurately predicts train wheel diameter. This method enhances train speed and location accuracy by addressing wheel wear, reducing manual work.

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Railway Engineering

Background:

  • Train wheel diameter dynamically decreases with mileage, impacting speed and location accuracy.
  • Traditional Gaussian Process Regression (GPR) faces hyper-parameter challenges with standard optimization methods.
  • Accurate real-time wheel diameter monitoring is crucial for train operational efficiency.

Purpose of the Study:

  • To propose and evaluate a Fast Simulated Annealing-Gaussian Process Regression (FSA-GPR) algorithm for predicting train wheel diameter.
  • To address the limitations of traditional GPR optimization techniques.
  • To improve the accuracy of train speed and location measurements.

Main Methods:

  • Development of an FSA-GPR algorithm for wheel diameter prediction.
  • Comparative analysis against traditional GPR, ABC-GPR, and GA-GPR algorithms.
  • Validation using real-world data from a DF11 train during a major repair period, assessed by RMSE, MAE, R2, and residual values.

Main Results:

  • The FSA-GPR algorithm demonstrated superior prediction accuracy compared to other evaluated methods.
  • Predictions from FSA-GPR closely matched the actual measured wheel diameter data.
  • Significant improvements in accuracy metrics (RMSE, MAE, R2) were observed with FSA-GPR.

Conclusions:

  • The proposed FSA-GPR algorithm effectively predicts train wheel diameter, mitigating issues from wheel wear.
  • Integration into vehicle-mounted systems can automate wheel diameter updates, reducing manual labor.
  • Enhanced accuracy in speed and position measurement contributes to improved train operational effectiveness.