Simulating dynamic RUL based CBM scheduling. A case study in the railway sector

Abstract

This paper uses continuous time computer simulation tools to analyze the maintenance scheduling problem of a fleet of assets that is subjected to a CBM (Condition-Based Maintenance) program for critical components under a maintenance 4.0 environment. Detection of component anomalies, their diagnosis and prognosis are considered as built-in capabilities of the organization. Once the remaining useful life (RUL) of a component at risk is known, the organization must react and determine when the component’s on-condition maintenance can be released. Maintenance on-condition activities are released controlling a variable named accumulated excess of anomalies over the CBM capacity constraint. When the existing CBM capacity is not enough to service all the components with their lowest possible RUL, components could be replaced with a higher RUL, as few times as possible, to ensure the largest component´s lifecycle. The paper explores the implications of different CBM capacity levels modelling a cost function. Cost function factors considered are cost of lost RUL, cost of CBM capacity, cost of overdue CBM and cost of asset unavailability due to overdue CBM. Empirically, the paper shows how capacity can be optimized to minimize this cost function. Once all different possibilities to schedule CBM activities are modeled, together with the cost of the selected CBM strategy, the paper compare results with those obtained for a base case where the organization could detect anomalies in components but not schedule CBM activities according to their RUL limitations and the maintenance organization capacity constraints. The paper demonstrates the different benefits of this opportunistic CBM task scheduling, according to assets stops for their predetermined PM activities. The tool that is developed has been tested in the railway sector, for a fleet of trains. Interesting results are obtained for different strategies, and they are discussed to understand possible implications of changes in the different factors and parameters of the problem.