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Identifying the mixed effects of unobserved and observed risk factors on the reliability of mining hauling system

Author

Listed:
  • Zeynab Allahkarami

    (University of Tarbiat Modares)

  • Ahmad Reza Sayadi

    (University of Tarbiat Modares)

  • Behzad Ghodrati

    (Luleå University of Technology)

Abstract

Reliability is widely used as a performance indicator of mining equipment to achieve a cost-effective maintenance plan. Reliability is a function of time as well as environmental and operational factors. Applying an adequate model by taking into account the mentioned factors is vital to ensure an accurate estimation of reliability characteristics. The aim of this study is to investigate the application of mixed frailty model to describe both observed and unobserved heterogeneity in reliability analysis of mining equipment. The capability of the model is assessed using field data from a fleet of dump trucks in an open-pit mine. The results indicate that the proposed model is superior to the traditional Cox model when data are heterogeneous. The results also show that the operator's skill and road conditions have a significant effect on the reliability of dump trucks.

Suggested Citation

  • Zeynab Allahkarami & Ahmad Reza Sayadi & Behzad Ghodrati, 2021. "Identifying the mixed effects of unobserved and observed risk factors on the reliability of mining hauling system," 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(2), pages 281-289, April.
  • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:2:d:10.1007_s13198-021-01073-3
    DOI: 10.1007/s13198-021-01073-3
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    References listed on IDEAS

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    1. Behzad Ghodrati, 2011. "Efficient Product Support—Optimum and Realistic Spare Parts Forecasting," Springer Series in Reliability Engineering, in: Lotfi Tadj & M.-Salah Ouali & Soumaya Yacout & Daoud Ait-Kadi (ed.), Replacement Models with Minimal Repair, pages 225-269, Springer.
    2. Slimacek, Vaclav & Lindqvist, Bo Henry, 2016. "Nonhomogeneous Poisson process with nonparametric frailty," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 14-23.
    3. Cha, Ji Hwan & Finkelstein, Maxim, 2014. "Some notes on unobserved parameters (frailties) in reliability modeling," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 99-103.
    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. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, July.
    6. Finkelstein, Maxim, 2007. "Imperfect repair and lifesaving in heterogeneous populations," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1671-1676.
    7. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    8. 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.
    9. Amin Moniri-Morad & Mohammad Pourgol-Mohammad & Hamid Aghababaei & Javad Sattarvand, 2019. "Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment," Journal of Risk and Reliability, , vol. 233(4), pages 593-604, August.
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