Application of Belief Theories for Railway Track Defect Detection

  • Alain Rivero SNCF-Reseau, Direction Generale Industrielle et Ingenierie, DepartmentIP3M DM MATRICE, Paris, France.
  • Sasa Radosavljevic SNCF-Réseau, Direction Générale Industrielle et Ingénierie, Département IP3M DM MATRICE, Paris, France.
  • Philippe Vanheeghe Univ. Lille, CNRS, Centrale Lille, UMR – CRIStAL, Lille, France.
Keywords: Belief theory, Yolo network, UberNet architecture, Data fusion, Infrastructure monitoring

Abstract

Faced with increasing traffic, railway infrastructures are encountering growing demands, particularly in high-traffic areas. In this context, rail and sleepers emerge as the components most susceptible to failure. To assist infrastructure managers (IM) in optimizing network maintenance, we have explored a novel method for detecting critical defects on the track. The objective is to develop a process for real-time analysis of railway infrastructure that is both frugal and efficient and can be installed on board commercial trains. This new infrastructure monitoring system integrates deep learning networks with a data fusion model based on belief theory. By modeling the decision-making process of a human operator, this processing chain has achieved detection rates exceeding 90% for the five primary defects: defective fasteners, broken fishplates and rails, surface defects, and missing nuts.

Published
2024-05-27