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An integrated systemic model for optimization of condition-based maintenance with human error

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  • Asadzadeh, S.M.
  • Azadeh, A.

Abstract

This paper proposes an integrated systemic model for the integration of human reliability model with condition based maintenance (CBM) optimization. The problem of CBM optimization is formulated as finding the optimum parameters of a function for condition monitoring (CM) scheduling so that the average unit cost (AUC) of CBM system is minimized. The concept of functional resonance is employed to analyze human-induced failure scenarios emergent from erroneous functional dependencies. To quantify human reliability in CBM, the functional characteristics of human error in CBM as well as the main performance influencing factors (PIFs) are identified. The algorithms of diagnostics and prognostics are integrated in the simulation model of CBM. Then an exact simulation-optimization algorithm based on the use of two joint Fibonacci algorithms is proposed for global optimization of CM scheduling with human error. A sensitivity analysis has been performed based on the newly developed model considering multiple levels of human errors in CBM functions to observe the effects of human errors on overall system cost. The model is also useful in demonstrating the importance and effects of improving human and organizational aspects as well as technical aspects such as the accuracy and relevance of CM technology and the accuracy of prognostics algorithm.

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  • Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    • Handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:117-131
    DOI: 10.1016/j.ress.2013.11.008
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    2. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
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    5. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    6. Hesabi, Hadis & Nourelfath, Mustapha & Hajji, Adnène, 2022. "A deep learning predictive model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    8. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    9. Azadeh, A. & Asadzadeh, S.M. & Salehi, N. & Firoozi, M., 2015. "Condition-based maintenance effectiveness for series–parallel power generation system—A combined Markovian simulation model," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 357-368.
    10. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    11. Juan Zhao & Ehmet Kasim, 2023. "Study of Dynamic Solutions for Human–Machine System with Human Error and Common-Cause Failure," Mathematics, MDPI, vol. 11(12), pages 1-29, June.
    12. Alrabghi, Abdullah & Tiwari, Ashutosh, 2016. "A novel approach for modelling complex maintenance systems using discrete event simulation," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 160-170.

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