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Engineering Asset Management 2016 pp 197–205Cite as

Addressing Missing Data for Diagnostic and Prognostic Purposes

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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

One of the major targets in industry is minimising the downtime of a machine while maximising its availability, with maintenance considered as a key aspect towards achieving this objective. Condition based maintenance and prognostics and health management, which relies on the concepts of diagnostics and prognostics, is a policy that has been gaining ground over several years. The successful implementation of this methodology is heavily dependent on the quality of data used which can be undermined in scenarios where there is missing data. This issue may compromise the information contained within a data set, thus having a significant effect on the conclusions that can be drawn, hence it is important to find suitable techniques to address this matter. To date a number of methods to recover such data, called imputation techniques, have been proposed. This paper reviews the most widely used methodologies and presents a case study using actual industrial centrifugal compressor data, in order to identify the most suitable technique.

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Author information

Authors and Affiliations

  1. School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK

    Panagiotis Loukopoulos, Suresh Sampath & Pericles Pilidis

  2. Shell Global Solutions, Rijswijk, Netherlands

    George Zolkiewski & Ian Bennett

  3. School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, UK

    Fang Duan & David Mba

Authors
  1. Panagiotis Loukopoulos
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  2. George Zolkiewski
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  3. Ian Bennett
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  4. Suresh Sampath
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  5. Pericles Pilidis
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  6. Fang Duan
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  7. David Mba
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Corresponding author

Correspondence to Panagiotis Loukopoulos .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada

    Ming J. Zuo

  2. Brisbane, Queensland, Australia

    Lin Ma

  3. Asset Institute, Brisbane, Queensland, Australia

    Joseph Mathew

  4. Institute of Reliability Engineering, UESTC, Sichuan, China

    Hong-Zhong Huang

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Loukopoulos, P. et al. (2018). Addressing Missing Data for Diagnostic and Prognostic Purposes. In: Zuo, M., Ma, L., Mathew, J., Huang, HZ. (eds) Engineering Asset Management 2016. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-62274-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-62274-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62273-6

  • Online ISBN: 978-3-319-62274-3

  • eBook Packages: EngineeringEngineering (R0)

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