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Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system

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  • Regattieri, A.
  • Manzini, R.
  • Battini, D.

Abstract

The reliability analysis of the critical components is the basic way to establish the efficiency of complex systems. For this issue, it is very important to select capable methods for service data collection as well as for analysis. The study introduces a framework defining a general approach for Failure Process Modeling (FPM). The paper also discusses the critical role of censored data and the need for a continuous and repetitive application of the proposed approach during the service life of systems.

Suggested Citation

  • Regattieri, A. & Manzini, R. & Battini, D., 2010. "Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1093-1102.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:10:p:1093-1102
    DOI: 10.1016/j.ress.2010.05.001
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    References listed on IDEAS

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    1. Barabady, Javad & Kumar, Uday, 2008. "Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 647-653.
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    4. Viertävä, Janne & Vaurio, Jussi K., 2009. "Testing statistical significance of trends in learning, ageing and safety indicators," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1128-1132.
    5. Zhang, L.F. & Xie, M. & Tang, L.C., 2006. "Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 930-939.
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    Citations

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    Cited by:

    1. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    2. 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.
    3. Taghipour, Sharareh & Banjevic, Dragan, 2011. "Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1340-1348.
    4. Kabir, Elnaz & Guikema, Seth & Kane, Brian, 2018. "Statistical modeling of tree failures during storms," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 68-79.
    5. Regattieri, A. & Giazzi, A. & Gamberi, M. & Gamberini, R., 2015. "An innovative method to optimize the maintenance policies in an aircraft: General framework and case study," Journal of Air Transport Management, Elsevier, vol. 44, pages 8-20.
    6. Lin, Yi-Kuei & Chang, Ping-Chen, 2012. "Evaluate the system reliability for a manufacturing network with reworking actions," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 127-137.
    7. R. Jamshidi & Mir Seyyed Esfahani, 2014. "Human resources scheduling to improve the product quality according to exhaustion limit," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 1028-1041, October.

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