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Age replacement policy in the case of no data: the effect of Weibull parameter estimation

Author

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  • Fabio Sgarbossa
  • Ilenia Zennaro
  • Eleonora Florian
  • Martina Calzavara

Abstract

Age replacement is a common maintenance policy when wear-out failures occur, and it is characterised by periodic replacement of components. Data on time to failure (TTF), often modelled with the Weibull function, are necessary for estimating optimal replacement intervals to minimise the total maintenance costs. In many cases, such as new components, new machines or new installations, no TTF data are available, so the Weibull parameters and optimal replacement interval cannot be estimated. To overcome this problem, these parameters can be assessed from the experience of the maintenance engineers and technicians. The aim of this study is investigating the relationship between the error in parameter estimation and additional maintenance costs related to this error. Analysis of variance (ANOVA) and multifactorial analysis are carried out for investigating the influence of these estimations on the final costs. Economic decision maps are introduced for supporting maintenance engineering in defining the maintenance policy with minimal additional cost in the case of no data being available. The analysis shows that, when no data are available, the application of the age replacement policy can result in a global saving of more than 50% compared with corrective maintenance.

Suggested Citation

  • Fabio Sgarbossa & Ilenia Zennaro & Eleonora Florian & Martina Calzavara, 2020. "Age replacement policy in the case of no data: the effect of Weibull parameter estimation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(19), pages 5851-5869, October.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:19:p:5851-5869
    DOI: 10.1080/00207543.2019.1660824
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    Cited by:

    1. van Staden, Heletjé E. & Deprez, Laurens & Boute, Robert N., 2022. "A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1079-1096.
    2. Yassine Eddouh & Abdelmajid Daya & Rabie Elotmani & Abdelhamid Touache, 2023. "Age replacement and inspection models for estimating optimal maintenance cost: numerical performance comparisons with a case study from chemical industries," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1354-1369, August.

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