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Analysing maintenance data to gain insight into systems performance

Author

Listed:
  • J Ansell

    (The University of Edinburgh)

  • T Archibald

    (The University of Edinburgh)

  • J Dagpunar

    (The University of Edinburgh)

  • L Thomas

    (University of Southampton)

  • P Abell

    (Yorkshire Water plc)

  • D Duncalf

    (Yorkshire Water plc)

Abstract

The high cost of maintenance in the processing industry implies the need for optimal planning of maintenance strategy. In order to achieve this there is a need to understand the underlying failure processes, which are often very complex. In this paper, a new semi-parametric approach, combining Cox regression with density kernal smoothing, is introduced to estimate the underlying performance. The approach has been applied to several processes and it allowed insight into each process, which would not have been achieved if traditional approaches had been used. Particularly, the refurbishment of processes had a significant impact on the rate failure. This paper concludes by assessing this impact of refurbishment on the maintenance programme.

Suggested Citation

  • J Ansell & T Archibald & J Dagpunar & L Thomas & P Abell & D Duncalf, 2003. "Analysing maintenance data to gain insight into systems performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(4), pages 343-349, April.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:4:d:10.1057_palgrave.jors.2601496
    DOI: 10.1057/palgrave.jors.2601496
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    References listed on IDEAS

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    1. Rommert Dekker & Ralph Wildeman & Frank Duyn Schouten, 1997. "A review of multi-component maintenance models with economic dependence," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 45(3), pages 411-435, October.
    2. Love, C. E. & Zhang, Z. G. & Zitron, M. A. & Guo, R., 2000. "A discrete semi-Markov decision model to determine the optimal repair/replacement policy under general repairs," European Journal of Operational Research, Elsevier, vol. 125(2), pages 398-409, September.
    3. A H Christer, 1999. "Developments in delay time analysis for modelling plant maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1120-1137, November.
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    Cited by:

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    2. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.

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