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Single versus mixture Weibull distributions for nonparametric satellite reliability

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  • Castet, Jean-Francois
  • Saleh, Joseph H.

Abstract

Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.

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  • Castet, Jean-Francois & Saleh, Joseph H., 2010. "Single versus mixture Weibull distributions for nonparametric satellite reliability," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 295-300.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:295-300
    DOI: 10.1016/j.ress.2009.10.001
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    1. Castet, Jean-Francois & Saleh, Joseph H., 2009. "Satellite and satellite subsystems reliability: Statistical data analysis and modeling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1718-1728.
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    Cited by:

    1. Bouwmeester, J. & Menicucci, A. & Gill, E.K.A., 2022. "Improving CubeSat reliability: Subsystem redundancy or improved testing?," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Damircheli, Mahrad & Fakoor, Mahdi & Yadegari, Hamed, 2020. "Failure assessment logic model (FALM): A new approach for reliability analysis of satellite attitude control subsystem," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Franko, Mitja & Nagode, Marko, 2015. "Probability density function of the equivalent stress amplitude using statistical transformation," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 118-125.
    4. Lin, Kunsong & Chen, Yunxia & Xu, Dan, 2017. "Reliability assessment model considering heterogeneous population in a multiple stresses accelerated test," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 134-143.
    5. Asadi, Majid & Ebrahimi, Nader & Soofi, Ehsan S. & Zohrevand, Younes, 2016. "Jensen–Shannon information of the coherent system lifetime," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 244-255.
    6. Róbert Csalódi & Zoltán Birkner & János Abonyi, 2021. "Learning Interpretable Mixture of Weibull Distributions—Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths," Data, MDPI, vol. 6(12), pages 1-11, November.
    7. Cavalcante, C.A.V. & Lopes, R.S. & Scarf, P.A., 2018. "A general inspection and opportunistic replacement policy for one-component systems of variable quality," European Journal of Operational Research, Elsevier, vol. 266(3), pages 911-919.
    8. Santosh B. Rane & Prathamesh R. Potdar & Suraj Rane, 2019. "Accelerated life testing for reliability improvement: a case study on Moulded Case Circuit Breaker (MCCB) mechanism," 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. 10(6), pages 1668-1690, December.
    9. Scarf, P.A. & Cavalcante, C.A.V. & Lopes, R.S., 2019. "Delay-time modelling of a critical system subject to random inspections," European Journal of Operational Research, Elsevier, vol. 278(3), pages 772-782.
    10. Lowe, Christopher J. & Macdonald, Malcolm, 2020. "Space mission resilience with inter-satellite networking," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    11. Jung, Seunghwa & Choi, Jihwan P., 2019. "Predicting system failure rates of SRAM-based FPGA on-board processors in space radiation environments," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 374-386.
    12. Guikema, S.D. & Quiring, S.M., 2012. "Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 178-182.
    13. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
    14. Laurent Bordes & Didier Chauveau, 2016. "Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data," Computational Statistics, Springer, vol. 31(4), pages 1513-1538, December.
    15. Kowal, Karol, 2022. "Lifetime reliability and availability simulation for the electrical system of HTTR coupled to the electricity-hydrogen cogeneration plant," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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