Fault diagnosis in railway track circuits using Dempster–Shafer classifier fusion

Latifa Oukhellou (1) , Alexandra Debiolles (2) , Thierry Denoeux (3) , Patrice Aknin (1)

1 INRETS/LTN - Laboratoire des Technologies Nouvelles
2 ingénierie IG SF 43
3 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

This paper addresses the problem of fault detection and isolation in railway track circuits. A track circuit can be considered as a large-scale system composed of a series of trimming capacitors located between a transmitter and a receiver. A defective capacitor affects not only its own inspection data (short circuit current) but also the measurements related to all capacitors located downstream (between the defective capacitor and the receiver). Here, the global fault detection and isolation problem is broken down into several local pattern recognition problems, each dedicated to one capacitor. The outputs from local neural network or decision tree classifiers are expressed using the Dempster–Shafer theory and combined to make a final decision on the detection and localization of a fault in the system. Experiments with simulated data show that correct detection rates over 99% and correct localization rates over 92% can be achieved using this approach, which represents a major improvement over the state of the art reference method.

Mots clés: Classifier fusion; Belief functions; Neural networks; Evidence theory; Transferable belief model; Fault detection and isolation; Pattern recognition

Domaines: Intelligence artificielle [cs.AI]

Latifa Oukhellou, Alexandra Debiolles, Thierry Denoeux, Patrice Aknin. Fault diagnosis in railway track circuits using Dempster–Shafer classifier fusion. Engineering Applications of Artificial Intelligence, 2010, 23 (1), pp.117-128. ⟨10.1016/j.engappai.2009.06.005⟩. ⟨hal-00467941⟩