A mixture of Kalman filters for online monitoring of railway switches
Résumé
Assessing the operating state of the railway infrastructure and rolling stock using condition measurements acquired through embedded sensors has become a powerful decision-making support for preventive maintenance strategies. This article introduces a dynamic approach for the online monitoring of railway switch operations. The method is based on modeling the power consumption curves acquired during successive switch operations using conjointly five polynomial regression models whose coefficients are dynamically estimated across a sequence of curves. The experimental study conducted on two real power consumption curve sequences from the French high speed network has shown encouraging results in terms of characterization of the temporal evolution of railway switch operations.