A novel approach to diagnostic and prognostic evaluations applied to railways: A real case study

Abstract

This paper presents qualitative and quantitative analyses of the action of points, a critical component of railway networks. They allow diagnostic and prognostic evaluations of the points using health monitoring systems, i.e. they allow the state of the system at a desired moment to be evaluated and the forecasting of the future condition of the system. The main objective is to increase the reliability, availability, safety and maintainability of these systems. A novel approach for maintenance management based on fault tree analysis is proposed. A binary decision diagram (BDD) approach is proposed for the qualitative analysis of the fault trees. The BDD obtains a Boolean expression for the fault tree. An optimal ordering of events is required in order to obtain an efficient conversion from a fault tree to a BDD, with the AND method being used for this purpose. Each event is classified based on its importance to the fault tree. It is studied using the Birnbaum and Criticality importance measures, using the Boolean expressions in order to perform accurate diagnostics and valuable prognostics on the state of the system. The presented approach allows the failure probability of the system to be determined along with importance measures obtained by considering variable time increments, e.g. shorter periods at the beginning and end of the life cycle of an event. A real case study on a set of M63 points is presented. The results provide useful information that can be used to support operations and the planning of maintenance tasks. The approach creates a methodology to establish effective maintenance planning, as it is a flexible and simple method that takes into account a nonlinear system that leads to an optimal allocation of resources. Finally, the conditions for an optimized online decision-making process are achieved.

Keywords

Maintenance management Birnbaum criticality diagnostics prognostics fault tree binary decision diagrams point machine

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