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Modelling Techniques to Support the Adoption of Predictive Maintenance

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Complex Engineering Service Systems

Part of the book series: Decision Engineering ((DECENGIN))

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Abstract

Contracting for availability and contracting for capability are becoming increasingly common practices in the defence world. With these new service-oriented contracts, the responsibility for through-life support, including maintenance, has been shifted from the user to the service provider. In this new environment, innovative approaches to improving maintenance and reliability are necessary and create new, unique opportunities for value co-creation between stakeholders. This chapter focuses on investigating the applicability and implementation of an approach to predictive maintenance which combines prognostic modelling with Condition Based Maintenance (CBM) and its role in providing improved service provision for the repair and maintenance of complex systems. The role of prognostic modelling and Health and Usage Monitoring Systems as the emerging technologies that enable a value-oriented approach to maintenance are discussed. Bayesian networks are discussed as a modelling framework that is appropriate to capture uncertainties related to predictive maintenance. Special focus is placed on reviewing practical challenges and proposing solutions to them. The discussion is summarised in the form of a practitioner’s guide to implementing prognostic modelling and CBM.

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Authors and Affiliations

  1. Department of Engineering Systems and Management, Defence Academy of the UK, Cranfield University, Shrivenham, SN6 8LA, UK

    Ken R. McNaught & Adam T. Zagorecki

Authors
  1. Ken R. McNaught
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  2. Adam T. Zagorecki
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Corresponding author

Correspondence to Ken R. McNaught .

Editor information

Editors and Affiliations

  1. , Streatham Court, University of Exeter Business School, Rennes Drive, Exeter, EX4 4PU, United Kingdom

    Irene Ng

  2. , Bristol Business School, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, United Kingdom

    Glenn Parry

  3. PaCT Laboratory, Department of Psychology, Northumbria University, Newcastle upon Tyne, NE1 8ST, United Kingdom

    Peter Wild

  4. , Institute for Manufacturing, University of Cambridge, Charles Babbage Road 17, Cambridge, CB3 0FS, United Kingdom

    Duncan McFarlane

  5. , Institute of Manufacturing, University of Cambridge, Charles Babbage Road 17, Cambridge, CB3 0FS, United Kingdom

    Paul Tasker

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© 2011 Springer-Verlag London Limited

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McNaught, K.R., Zagorecki, A.T. (2011). Modelling Techniques to Support the Adoption of Predictive Maintenance. In: Ng, I., Parry, G., Wild, P., McFarlane, D., Tasker, P. (eds) Complex Engineering Service Systems. Decision Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-189-9_15

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  • DOI: https://doi.org/10.1007/978-0-85729-189-9_15

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-188-2

  • Online ISBN: 978-0-85729-189-9

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