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Maintainability analysis considering time-dependent and time-independent covariates

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  • Barabadi, Abbas
  • Barabady, Javad
  • Markeset, Tore

Abstract

Traditional parametric methods for assessing maintainability most often only consider time to repair (TTR) as a single explanatory variable. However, to predict availability more precisely for high availability systems, a better model is needed to quantify the effect of operational environment on maintainability. The proportional repair model (PRM), which is developed based on proportional hazard model (PHM), may be used to analyze maintainability in the present of covariates. In the PRM, the effect of covariates is considered to be time independent. However this assumption may not be valid for some situations. The aim of this paper is to develop the Cox regression model and its extension in the presence of time-dependent covariates for determining maintainability. A simple case study is used to demonstrate how the model can be applied in a real case.

Suggested Citation

  • Barabadi, Abbas & Barabady, Javad & Markeset, Tore, 2011. "Maintainability analysis considering time-dependent and time-independent covariates," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 210-217.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:1:p:210-217
    DOI: 10.1016/j.ress.2010.08.007
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    References listed on IDEAS

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    Cited by:

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    7. Rezgar Zaki & Abbas Barabadi & Ali Nouri Qarahasanlou & A. H. S. Garmabaki, 2019. "A mixture frailty model for maintainability analysis of mechanical components: a case study," 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 1646-1653, December.
    8. Parada Puig, J.E. & Basten, R.J.I., 2015. "Defining line replaceable units," European Journal of Operational Research, Elsevier, vol. 247(1), pages 310-320.
    9. Luo, Xu & Ge, Zhexue & Zhang, ShiGang & Yang, Yongmin, 2021. "A method for the maintainability evaluation at design stage using maintainability design attributes," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. 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.
    11. 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.
    12. Opabola, Eyitayo A. & Galasso, Carmine, 2024. "A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    14. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. 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.
    16. Barker, Kash & Baroud, Hiba, 2014. "Proportional hazards models of infrastructure system recovery," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 201-206.
    17. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    18. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.
    19. 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.
    20. Moreu De Leon, Pedro & González-Prida Díaz, Vicente & Barberá Martínez, Luis & Crespo Márquez, Adolfo, 2012. "A practical method for the maintainability assessment in industrial devices using indicators and specific attributes," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 84-92.

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