American Journal of Theoretical and Applied Statistics

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Optimal Replacement Age and Maintenance Cost: A Case Study

Received: 21 February 2015    Accepted: 04 March 2015    Published: 14 March 2015
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Abstract

Maintenance plays a very vital role throughout an equipment/system’s planned life-cycle. Maintenance costs contribute a major portion of the life cycle costs of an equipment or system. This paper analyzes a set of failure data of a particular type of battery that used in automobile and/or trailer and proposes the optimal maintenance age at which the non-failed battery be maintained. A 2-fold Weibull mixture distribution is selected as the suitable distribution for the lifetime of the battery. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters of the mixture distribution. The research will be of interest for effective maintenance management to maintenance engineers and managers working in battery manufacturing industry, as well as customers and service providers.

DOI 10.11648/j.ajtas.20150402.12
Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 2, March 2015)
Page(s) 53-57
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Maintenance, 2-fold WeibullMixture Distribution, EM Algorithm, Optimal Replacement Age

References
[1] Aditya, P. and Uday, K., 2006. Maintenance performance measurement (MPM): issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), pp.239-251.
[2] Blischke, W.R., Karim, M.R. and Murthy, D.N.P. (2011), Warranty Data Collection and Analysis, Springer Verlag, London.
[3] Duffuaa,S.O., Raouf,A.andCambell,J.D. (1999), planning and control of maintenance systems:modeling and analysis, John Wiley & sons.
[4] Eichler, C. (1990). Instandhaltungstechniken. VerlagTechnik GmbH, Berlin.
[5] Hale AH, Heming BHJ, Smit K, Rodenburg FG, Van Leeuwen ND (1998) Evaluating Safety in the Management of Maintenance Activities in the Process Industry. SafSci28(1): 21–44.
[6] Mobley, R. K., 2004. Maintenance fundamentals. 2nd ed. Burlington, Mass.: Butterworth Heinemann.
[7] U. Kumar, D. Galar, A. Parida, C.Stenström, L. Berges, Maintenance Performance Metrics: A State of the Art Review, ISBN 978-91-7439-379-8.
Author Information
  • Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh

  • Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh

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  • APA Style

    Nayeema Sultana, Md. RezaulKarim. (2015). Optimal Replacement Age and Maintenance Cost: A Case Study. American Journal of Theoretical and Applied Statistics, 4(2), 53-57. https://doi.org/10.11648/j.ajtas.20150402.12

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    ACS Style

    Nayeema Sultana; Md. RezaulKarim. Optimal Replacement Age and Maintenance Cost: A Case Study. Am. J. Theor. Appl. Stat. 2015, 4(2), 53-57. doi: 10.11648/j.ajtas.20150402.12

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    AMA Style

    Nayeema Sultana, Md. RezaulKarim. Optimal Replacement Age and Maintenance Cost: A Case Study. Am J Theor Appl Stat. 2015;4(2):53-57. doi: 10.11648/j.ajtas.20150402.12

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  • @article{10.11648/j.ajtas.20150402.12,
      author = {Nayeema Sultana and Md. RezaulKarim},
      title = {Optimal Replacement Age and Maintenance Cost: A Case Study},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {2},
      pages = {53-57},
      doi = {10.11648/j.ajtas.20150402.12},
      url = {https://doi.org/10.11648/j.ajtas.20150402.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20150402.12},
      abstract = {Maintenance plays a very vital role throughout an equipment/system’s planned life-cycle. Maintenance costs contribute a major portion of the life cycle costs of an equipment or system. This paper analyzes a set of failure data of a particular type of battery that used in automobile and/or trailer and proposes the optimal maintenance age at which the non-failed battery be maintained. A 2-fold Weibull mixture distribution is selected as the suitable distribution for the lifetime of the battery. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters of the mixture distribution. The research will be of interest for effective maintenance management to maintenance engineers and managers working in battery manufacturing industry, as well as customers and service providers.},
     year = {2015}
    }
    

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