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

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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.

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References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  MathSciNet  MATH  Google Scholar 

  • Asadzadeh S, Aghaie A, Shahriari H (2014a) Using frailty models to account for heterogeneity in multistage manufacturing and service processes. Qual Quant 48(2):593–604

    Article  Google Scholar 

  • Asadzadeh S, Aghaie A, Shahriari H, Niaki STA (2014b) The application of proportional hazards and frailty models to multistage processes surveillance. Int J Adv Manuf Technol 74(1):461–470

    Article  Google Scholar 

  • Barabadi A, Barabady J, Markeset T (2011a) Maintainability analysis considering time-dependent and time-independent covariates. Reliab Eng Syst Saf 96(1):210–217

    Article  Google Scholar 

  • Barabadi A, Barabady J, Markeset T (2011b) A methodology for throughput capacity analysis of a production facility considering environment condition. Reliab Eng Syst Saf 96(12):1637–1646

    Article  Google Scholar 

  • Barabadi A, Barabady J, Markeset T (2014) Application of reliability models with covariates in spare part prediction and optimization—a case study. Reliab Eng Syst Saf 123:1–7

    Article  Google Scholar 

  • Bisgaard S, Kulahci M (2011) Time series analysis and forecasting by example. Wiley, New York

    Book  MATH  Google Scholar 

  • Cha JH, Finkelstein M (2014) Some notes on unobserved parameters (frailties) in reliability modeling. Reliab Eng Syst Saf 123:99–103

    Article  Google Scholar 

  • Clayton DG (1978) A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65(1):141–151

    Article  MathSciNet  MATH  Google Scholar 

  • Cox DR (1972) Regression models and life tables (with discussion): Series B (Methodological). J R Stat Soc 34(2):187–220

    Google Scholar 

  • Duchateau L, Janssen P (2007) The frailty model. Springer, New York

    MATH  Google Scholar 

  • Finkelstein M (2007) Imperfect repair and lifesaving in heterogeneous populations. Reliab Eng Syst Saf 92(12):1671–1676

    Article  Google Scholar 

  • Ghodrati B (2005) Reliability and operating environment based spare parts planning. PhD diss., Lulea, University of Technology, Lulea

  • Ghodrati B (2006) Weibull and exponential renewal models in spare parts estimation: a comparison. Int J Perform Eng 2(2):135–147

    Google Scholar 

  • Ghodrati B (2011) Efficient product support—optimum and realistic spare parts forecasting. Replacement models with minimal repair. Springer, London, pp 225–269

    Chapter  Google Scholar 

  • Ghodrati B, Ahmadzadeh F, Kumar U (2012a) Remaining useful life estimation of mining equipment: a case study. In: International Symposium on Mine Planning and Equipment Selection (ISMPES), New Delhi, pp 83–94

  • Ghodrati B, Benjevic D, Jardine A (2012b) Product support improvement by considering system operating environment: a case study on spare parts procurement. Int J Qual Reliab Manag 29(4):436–450

    Article  Google Scholar 

  • Ghodrati B, Hoseinie SH, Kumar U (2017) Context-driven mean residual life estimation of mining machinery. Int J Min Reclam Environ 32(7):1–9

    Google Scholar 

  • Giorgio M, Guida M, Pulcini G (2014) Repairable system analysis in presence of covariates and random effects. Reliab Eng Syst Saf 131:271–281

    Article  Google Scholar 

  • Hall RA, Daneshmend LK (2003) Reliability modelling of surface mining equipment: data gathering and analysis methodologies. Int J Surf Min Reclam Environ 17(3):139–155

    Article  Google Scholar 

  • Ho M, Hodkiewicz M (2013) Factors that influence failure behaviour and remaining useful life of mining equipment components. Adv Mech Eng 5:9

    Google Scholar 

  • Hougaard P (2000) Analysis of multivariate survival data. Springer, New York

    Book  MATH  Google Scholar 

  • Kleinbaum DG, Klein M (2012) Survival analysis: a self-learning text, 3rd edn. Springer, New York

    Book  MATH  Google Scholar 

  • Kumar D, Klefsjö B, Kumar U (1992) Reliability analysis of power transmission cables of electric mine loaders using the proportional hazards model. Reliab Eng Syst Saf 37(3):217–222

    Article  Google Scholar 

  • Kumar U (1990) Reliability analysis of load-haul-dump machines. Luleå University of Technologyy, Luleå

    Google Scholar 

  • Lancaster T (1979) Econometric methods for the duration of unemployment. Econometrica 47(4):939

    Article  MATH  Google Scholar 

  • Li J, Ma S (2013) Survival analysis in medicine and genetics. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  • Moniri-Morad A, Pourgol-Mohammad M, Aghababaei H, Sattarvand J (2019) Reliability-based covariate analysis for complex systems in heterogeneous environment: case study of mining equipment. Proc Inst Mech Eng Part O: J Risk Reliab 233(4):593–604

    Article  Google Scholar 

  • Moniri-Morad A, Pourgol-Mohammad M, Aghababaeia H, Sattarvand J (2018) Reliability-based regression model for complex systems considering environmental uncertainties. In Probabilistic Safety Assessment and Management (PSAM 14), Los Angeles, CA.

  • Nouri Qarahasanlou A, Ataei M, Khaolukakaie R, Ghodrati B, Mokhberdoran M (2017) Maintainability measure based on operating environment, a case study: Sungun copper mine. J Min Environ 8(3):511–521

    Google Scholar 

  • Nouri Qarahasanlou A, Barabadi A, Ataei M, Einian V (2019) Spare part requirement prediction under different maintenance strategies. Int J Min Reclam Environ 33(3):169–182

    Article  Google Scholar 

  • Qarahasanlou AN, Khalokakaie R, Ataei M, Ghodrati B (2017) Operating environment-based availability importance measures for mining equipment (Case Study: Sungun Copper Mine). J Fail Anal Prev 17(1):56–67

    Article  Google Scholar 

  • Slimacek V, Lindqvist BH (2016) Nonhomogeneous Poisson process with nonparametric frailty. Reliab Eng Syst Saf 149:14–23

    Article  Google Scholar 

  • Slimacek V, Lindqvist BH (2016) Reliability of wind turbines modeled by a Poisson process with covariates, unobserved heterogeneity and seasonality. Wind Energy 19(11):1991–2002

    Article  Google Scholar 

  • Slimacek V, Lindqvist BH (2017) Nonhomogeneous Poisson process with nonparametric frailty and covariates. Reliab Eng Syst Saf 167:75–83

    Article  Google Scholar 

  • Therneau TM, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, Berlin

    Book  MATH  Google Scholar 

  • Therneau TM, Grambsch PM, Pankratz VS (2003) Penalized survival models and frailty. J Comput Graph Stat 12(1):156–175

    Article  MathSciNet  Google Scholar 

  • Vaupel JW, Manton KG, Stallard E (1979) The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16(3):439–454

    Article  Google Scholar 

  • Wienke A (2011) Frailty models in survival analysis. CRC Press, Boca Raton

    Google Scholar 

  • Xu R, Vaida F, Harrington DP (2009) Using profile likelihood for semiparametric model selection with application to proportional hazards mixed models. Stat Sin 19(2):819–842

    MathSciNet  MATH  Google Scholar 

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Correspondence to Ahmad Reza Sayadi.

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Allahkarami, Z., Sayadi, A.R. & Ghodrati, B. Identifying the mixed effects of unobserved and observed risk factors on the reliability of mining hauling system. Int J Syst Assur Eng Manag 12, 281–289 (2021). https://doi.org/10.1007/s13198-021-01073-3

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