Generalised fleet maintenance modelling

Reyes Campaña, Héctor Rodrigo (2019) Generalised fleet maintenance modelling. PhD thesis, University of Nottingham.

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Abstract

In the new global economy, asset management has become a central issue for the defence industry. It is well known that countries try to maintain the peace and security of their borders by investing in the weapon systems of their armed forces. Unfortunately, owning, managing, and maintaining a powerful fleet of defence vehicles in normal condition to work is extremely expensive. Although defence budgets remain a priority in almost all countries, during the last decade, budgets in this sector have continuously decreased. For example, budgets for military defence in South America have been severely affected by different external and internal economic factors, such as natural disasters and fluctuations in the raw materials price in the international market. In fact, maintaining the condition of a defence fleet on a low budget is a challenge for the armed forces of South American and developing countries.

Vehicle fleet maintenance management is an area of critical importance in defence, especially when military engineers are needed on site for making maintenance decisions. Knowledge about the real condition of fleets working in different areas could allow maintenance staff to manage a set of priorities and allocate resources for preventive and/or corrective maintenance actions. In addition, modelling maintenance processes and the estimation of Key Performance Indicators (KPIs) are crucial in the operational analysis and performance evaluation of land vehicle fleets.

On the modern battlefield, concepts related to logistics, fleet management and maintenance modelling have a real impact when a military fleet needs to achieve a mission. For this reason, modelling with Petri Net (PN) techniques can assist in the understanding of military fleet performance. This thesis presents three main innovations: a military fleet with a long stand by state, second hand military tanks being from the 1970s (no monitoring) and finally, no previous recorded data history.

This research provides an overview of fleet management concepts, as well as the Hierarchical Coloured Petri Nets (HCPN) technique for modelling the maintenance process in defence fleets. The project is aimed at developing an asset management methodology which will predict the operational capability of defence fleets in order to improve their availability and assist in making effective decisions about the maintenance process. This is especially important when fleets operate in different environments which could accelerate the deterioration of critical systems.

The current research work proposes a Fleet Maintenance Model (FMM) based on the Hierarchical Coloured Petri Nets (HCPN) method for managing military land vehicle fleets. The work highlights the significance of using quantitative techniques which can help to identify and analyse the performance of the defence fleet. The aim is to develop a Fleet Maintenance Model (FMM) for fleet management which will predict the operational capability of vehicle fleets in order to improve their availability and help in making effective decisions about the maintenance process and optimising the frequency of inspections during the life-cycle of vehicles. The mathematical and computational tools used for this study are discussed in detail. The preliminary results are studied to validate the model and to illustrate its capability. The conclusion of thesis summarises the work done and suggests directions for further study.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Andrews, John
Prescott, Darren
Keywords: Maintenance, asset management, fleet management, modelling, simulation, Petri Nets, key performance indicators
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
U Military science > U Military science (General)
Faculties/Schools: UK Campuses > Faculty of Engineering
Item ID: 55734
Depositing User: Reyes Campaña, Héctor
Date Deposited: 27 Sep 2021 14:33
Last Modified: 27 Sep 2021 14:35
URI: http://eprints.nottingham.ac.uk/id/eprint/55734

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