Dynamic Fleet Maintenance Management

Abstract

The increased prevalence of digitalisation in today’s industry allows a comprehensive understanding of the value of the assets for the organisations. In this context, new technologies enable predictive maintenance to cover all activities from data acquisition and processing to maintenance decision-making advisory as output. This particularly expands to a wider view of ‘health management’ as opposed to a focus solely on maintenance at the fleet level. Consequently, decisions related to workload determination and operational scheduling must be aligned with asset condition assessments and maintenance strategies. Traditional fleet maintenance strategies are often reactive or rely on predetermined schedules, which can lead to inefficient resource allocation and increased operational costs. The paradigm shift towards a data-driven approach enables fleet management to dynamically respond to issues identified through sensors and algorithms insights. Furthermore, it allows fleet management to make integrated maintenance and operations decisions. However, despite the rapid technological change, a notable deficiency exists in the integration of predictive maintenance with predetermined preventive maintenance, existing limitations for maintenance resources in each depot, and fleet operational scheduling. This is due to the lack of a holistic strategic approach, and a criterion to stablish the optimal solution, thereby impeding value creation for businesses. The thesis presents a three stage model that (i) defines the operating context and maintenance resources (ii) evaluates feasible opportunistic maintenance timeslots to integrate predictive maintenance and (iii) allocates the assets of the fleet to operation, preventive and predictive maintenance, or being idle for a certain planning horizon. In addition, the thesis explores how the optimal allocation, and hence the value of this integrated approach is affected by the criticality of the components that are monitored, the quality of RUL prognosis, and the balance of maintenance costs and service risks. The applicability of the approach is demonstrated through a case study using a real industrial scenario of a fleet of high speed trains from Talgo in Spain. The thesis concludes that the proposed approach presents a holistic solution that allows to solve the research gap and integrate predictive maintenance scheduling with preventive maintenance, depot resources, and workload balance. Generally, highly critical assets justify the integration of predictive maintenance into the decision making process, as the savings considering the risk of failure accurately would compensate the cost of condition monitoring. This was proven with the model, and besides, components of medium criticality can also be quantified and justified for condition monitoring. Furthermore, nowadays due to the high demanding service contracts fleet managers may assume higher risks in order to fulfil service. This challenging balance is quantified by the model proposing the optimal fleet scheduling recommendation.