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Importance measures for multi-phase missions

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  • Vaurio, J.K.

Abstract

Phased missions consist of consecutive operational phases where the system logic and failure parameters can change between phases. A component can have different roles in different phases and the reliability function may have discontinuities at phase boundaries. An earlier method required NOT-gates and negations of events when calculating importance measures for such missions with non-repairable components. This paper suggests an exact method that uses standard fault tree techniques and Boolean algebra without any NOT-gates or negations. The criticalities and other importance measures can be obtained for events and components relevant to a single phase or to a transition between phases or over the whole mission. The method and importance measures are extended to phased missions with repairable components. Quantification of the reliability, the availability, the failure intensity and the total number of failures are described. New importance indicators defined for repairable systems measure component contributions to the total integrated unavailability, to the mission failure intensity and to the total number of mission failures.

Suggested Citation

  • Vaurio, J.K., 2011. "Importance measures for multi-phase missions," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 230-235.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:1:p:230-235
    DOI: 10.1016/j.ress.2010.07.002
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    1. Vaurio, Jussi K., 2010. "Ideas and developments in importance measures and fault-tree techniques for reliability and risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 99-107.
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    Cited by:

    1. Wu, Xin-yang & Wu, Xiao-yue & Balakrishnan, Narayanaswamy, 2018. "Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 266-276.
    2. Dui, Hongyan & Si, Shubin & Wu, Shaomin & Yam, Richard C.M., 2017. "An importance measure for multistate systems with external factors," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 49-57.
    3. Aliee, Hananeh & Borgonovo, Emanuele & Glaß, Michael & Teich, Jürgen, 2017. "On the Boolean extension of the Birnbaum importance to non-coherent systems," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 191-200.
    4. Lu, H.W. & Pan, H.Y. & He, L. & Zhang, J.Q., 2016. "Importance analysis of off-grid wind power generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 999-1007.
    5. Wu, Shaomin & Chen, Yi & Wu, Qingtai & Wang, Zhonglai, 2016. "Linking component importance to optimisation of preventive maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 26-32.
    6. Lyu, Dong & Si, Shubin, 2020. "Dynamic importance measure for the K-out-of-n: G system under repeated random load," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Zhu, Xiaoyan & Fu, Yuqiang & Yuan, Tao & Wu, Xinying, 2017. "Birnbaum importance based heuristics for multi-type component assignment problems," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 209-221.
    8. Lingling Bin & Haiyang Pan & Li He & Jijian Lian, 2019. "An Importance Analysis–Based Weight Evaluation Framework for Identifying Key Components of Multi-Configuration Off-Grid Wind Power Generation Systems under Stochastic Data Inputs," Energies, MDPI, vol. 12(22), pages 1-22, November.
    9. Tyrväinen, T., 2013. "Risk importance measures in the dynamic flowgraph methodology," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 35-50.
    10. Vaurio, Jussi K., 2016. "Importances of components and events in non-coherent systems and risk models," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 117-122.

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