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Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

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  • Amin Moniri-Morad
  • Mohammad Pourgol-Mohammad
  • Hamid Aghababaei
  • Javad Sattarvand

Abstract

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:4:p:593-604
    DOI: 10.1177/1748006X18807091
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    References listed on IDEAS

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    Cited by:

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    3. Jiang, Renyan & Li, Fengping & Xue, Wei & Cao, Yu & Zhang, Kunpeng, 2023. "A robust mean cumulative function estimator and its application to overhaul time optimization for a fleet of heterogeneous repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Wang, Rongxi & Li, Yufan & Xu, Jinjin & Wang, Zhen & Gao, Jianmin, 2022. "F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Hu, Wei & Yang, Zhaojun & Chen, Chuanhai & Wu, Yue & Xie, Qunya, 2021. "A Weibull-based recurrent regression model for repairable systems considering double effects of operation and maintenance: A case study of machine tools," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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