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A reliability decision framework for multiple repairable units

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  • Garmabaki, A.H.S.
  • Ahmadi, Alireza
  • Block, Jan
  • Pham, Hoang
  • Kumar, Uday

Abstract

In practice, the analyst is often dealing with multiple repairable units, installed in different positions or functioning under different operating conditions, and maintained by different disciplines. This paper presents a decision framework to identify an appropriate reliability model for massive multiple repairable units. It splits non-homogeneous failure data into homogeneous groups and classifies them based on their failure trends using statistical tests. The framework discusses different scenarios for analysing multiple repairable units, according to trend, intensity, and dependency of the units׳ failure data. The proposed framework has been verified in a fleet of aircraft and in two simulated data sets. The results show a reliability model of multiple repairable units may contain a mixture of different stochastic models. Considering single reliability models for such populations may cause erroneous calculation of the time to failure of a particular unit, which can, in turn, lead to faulty conclusions and decisions. When dealing with massive and non-homogeneous multiple repairable units, the application of the proposed framework can facilitate the selection of an appropriate reliability model.

Suggested Citation

  • Garmabaki, A.H.S. & Ahmadi, Alireza & Block, Jan & Pham, Hoang & Kumar, Uday, 2016. "A reliability decision framework for multiple repairable units," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 78-88.
    • Handle: RePEc:eee:reensy:v:150:y:2016:i:c:p:78-88
    DOI: 10.1016/j.ress.2016.01.020
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    References listed on IDEAS

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    1. Van Dyck, Jozef & Verdonck, Tim, 2014. "Precision of power-law NHPP estimates for multiple systems with known failure rate scaling," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 143-152.
    2. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    3. 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.
    4. Giorgio, M. & Guida, M. & Pulcini, G., 2014. "Repairable system analysis in presence of covariates and random effects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 271-281.
    5. 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.
    6. Barabadi, Abbas & Barabady, Javad & Markeset, Tore, 2014. "Application of reliability models with covariates in spare part prediction and optimization – A case study," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 1-7.
    7. Barabadi, Abbas & Tobias Gudmestad, Ove & Barabady, Javad, 2015. "RAMS data collection under Arctic conditions," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 92-99.
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    Cited by:

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    2. Rezgar Zaki & Abbas Barabadi & Javad Barabady & Ali Nouri Qarahasanlou, 2022. "Observed and unobserved heterogeneity in failure data analysis," Journal of Risk and Reliability, , vol. 236(1), pages 194-207, February.
    3. Reza Barabadi & Mohammad Ataei & Reza Khalokakaie & Ali Nouri Qarahasanlou, 2021. "Spare-part management in a heterogeneous environment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    4. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "Resilience analysis: A formulation to model risk factors on complex system resilience," 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. 12(5), pages 871-883, October.
    5. Awat Ghomghaleh & Reza Khaloukakaie & Mohammad Ataei & Abbas Barabadi & Ali Nouri Qarahasanlou & Omeid Rahmani & Amin Beiranvand Pour, 2020. "Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.
    6. Motahareh Sagharidooz & Hamzeh Soltanali & José Torres Farinha & Hugo D. N. Raposo & José Edmundo de-Almeida-e-Pais, 2024. "Reliability, Availability, and Maintainability Assessment-Based Sustainability-Informed Maintenance Optimization in Power Transmission Networks," Sustainability, MDPI, vol. 16(15), pages 1-22, July.
    7. Ali N Qarahasanlou & Abbas Barabadi & Yonas Z Ayele, 2018. "Production performance analysis during operation phase: A case study," Journal of Risk and Reliability, , vol. 232(6), pages 559-575, December.
    8. Slimacek, Vaclav & Lindqvist, Bo Henry, 2017. "Nonhomogeneous Poisson process with nonparametric frailty and covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 75-83.
    9. Barabadi, A. & Ayele, Y.Z., 2018. "Post-disaster infrastructure recovery: Prediction of recovery rate using historical data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 209-223.

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