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Intelligent Classification Method for Rail Defects in Magnetic Flux Leakage Testing Based on Feature Selection and

Kailun Ji1, Ping Wang1, Yinliang Jia1

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

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|July 12, 2025
PubMed
Summary

This study enhances rail magnetic flux leakage (MFL) detection accuracy using an optimized neural network. The improved framework significantly boosts classification performance for various rail defects, ensuring safer infrastructure.

Keywords:
feature selectionimbalanced dataintelligent classificationmagnetic flux leakage (MFL) testingmodel optimizationrail defect detection

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Area of Science:

  • Railway Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Rail integrity is crucial for transportation safety.
  • Magnetic Flux Leakage (MFL) is a key non-destructive testing method for rails.
  • Current MFL defect classification faces accuracy challenges, especially with limited data.

Purpose of the Study:

  • To develop an enhanced intelligent classification framework for rail MFL signals.
  • To improve the accuracy and robustness of defect detection in railway inspection.
  • To address the limitations of conventional methods in handling small-sample scenarios and complex defect types.

Main Methods:

  • Proposed an enhanced classification framework using a Particle Swarm Optimized Radial Basis Function neural network (PSO-RBF).
  • Implemented a dynamic PSO algorithm with adaptive learning factors and nonlinear inertia weight for RBF parameter optimization.
  • Employed a hierarchical feature processing strategy combining mutual information selection and correlation-based dimensionality reduction.
  • Incorporated adaptive model architecture adjustment for effective small-sample learning.

Main Results:

  • Achieved 87.5% accuracy on artificial defects, a 17.5% absolute improvement over conventional RBF.
  • Obtained macro-F1 score of 0.817 and Matthews Correlation Coefficient (MCC) of 0.733 for artificial defects.
  • For real-world limited samples, reached 80% accuracy, improved minority class (spalling) F1-score by 0.25, and reduced false alarms by 50%.

Conclusions:

  • The optimized PSO-RBF framework demonstrates superior capability in extracting and classifying MFL signal patterns.
  • The study sets a new benchmark for industrial rail inspection, particularly in discriminating between abrasions, spalling, indentations, and shelling defects.
  • The proposed methods offer a robust solution for accurate MFL-based rail defect classification, even with limited data.