Anticipating Railway Operation Disruption Events Based on the Analysis of Discrete-Event Diagnostic Data

Abstract

Public transport reliability is a highly important factor affecting passenger service quality, transportation mode choice, and operating costs. Unreliable service increases operating costs and reduces patronage. In railway systems reliability is significantly influenced by the technical reliability of infrastructure and rolling stock systems. To meet the stringent requirements on reliability and availability, many advanced railway systems and components include monitoring and diagnostic tools. The data generated by these systems can be supplied to data-based methods predicting reliability. Approaches to predict component failures and remaining useful life are usually based on continuously measured diagnostic signal data. The use of event-based diagnostic data is limited. This paper describes our research applying Echo-State Networks (ESN) in combination with Restricted Boltzmann Machines (RBM) and fuzzy logic to predict potential railway network disruptions based on discrete-event diagnostic data. The case study focuses on predicting impending failures of a train door system on the level of an individual system potentially causing disruption events of railway operations. The proposed approach achieved an average prediction accuracy of 97 %. The research results demonstrate the suitability of the proposed combination of methods for use in predicting railway operation disruption events. The findings show that the prediction of medium-term class event patterns is especially helpful since railway operators can use this information to take remedial actions to prevent the disruption.