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Optimal mission abort policy with multiple shock number thresholds

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  • Gregory Levitin
  • Maxim Finkelstein

Abstract

At many instances, it is important to keep a system from being destroyed or “lost†at a cost of not completing its mission. Therefore, to enhance survivability of many real-world critical systems (e.g. aircrafts and human space flight systems), mission abort procedures are often utilized in practice. In this article, in order to suggest an optimal abort policy, we first develop a methodology for obtaining the mission success probability and survivability of system experiencing both internal failures and external shocks. Each shock increases degradation in a system, and therefore, the number of experienced shocks can be considered as a decision parameter. We divide the mission time into a number of adjacent intervals and suggest a policy when a mission is aborted (and a rescue procedure is activated) if the number of shocks exceeds the value predetermined for each interval. We demonstrate the trade-off between system survivability and mission success probability that should be balanced by the proper choice of the mission abort policy. An illustrative example of a mission performed by an unmanned aerial vehicle is presented.

Suggested Citation

  • Gregory Levitin & Maxim Finkelstein, 2018. "Optimal mission abort policy with multiple shock number thresholds," Journal of Risk and Reliability, , vol. 232(6), pages 607-615, December.
  • Handle: RePEc:sae:risrel:v:232:y:2018:i:6:p:607-615
    DOI: 10.1177/1748006X17751496
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    References listed on IDEAS

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    1. Levitin, Gregory & Xing, Liudong & Amari, Suprasad V. & Dai, Yuanshun, 2013. "Reliability of non-repairable phased-mission systems with propagated failures," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 218-228.
    2. Wang, Chaonan & Xing, Liudong & Peng, Rui & Pan, Zhusheng, 2017. "Competing failure analysis in phased-mission systems with multiple functional dependence groups," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 24-33.
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    4. Toshio Nakagawa, 2007. "Shock and Damage Models in Reliability Theory," Springer Series in Reliability Engineering, Springer, number 978-1-84628-442-7, January.
    5. Levitin, Gregory & Finkelstein, Maxim, 2018. "Optimal mission abort policy for systems in a random environment with variable shock rate," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 11-17.
    6. Maxim Finkelstein, 2008. "Failure Rate Modelling for Reliability and Risk," Springer Series in Reliability Engineering, Springer, number 978-1-84800-986-8, January.
    7. Lu, Ji-Min & Wu, Xiao-Yue & Liu, Yiliu & Ann Lundteigen, Mary, 2015. "Reliability analysis of large phased-mission systems with repairable components based on success-state sampling," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 123-133.
    8. Maxim Finkelstein & Ji Hwan Cha, 2013. "Burn-in for Heterogeneous Populations," Springer Series in Reliability Engineering, in: Stochastic Modeling for Reliability, edition 127, chapter 0, pages 261-312, Springer.
    9. Levitin, Gregory & Finkelstein, Maxim & Dai, Yuanshun, 2017. "Redundancy optimization for series-parallel phased mission systems exposed to random shocks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 554-560.
    10. Wang, Chaonan & Xing, Liudong & Levitin, Gregory, 2015. "Probabilistic common cause failures in phased-mission systems," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 53-60.
    11. Levitin, Gregory & Finkelstein, Maxim, 2017. "Optimal backup in heterogeneous standby systems exposed to shocks," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 336-344.
    12. Maxim Finkelstein & Ji Hwan Cha, 2013. "Shocks as Burn-in," Springer Series in Reliability Engineering, in: Stochastic Modeling for Reliability, edition 127, chapter 0, pages 313-361, Springer.
    13. Yang, Li & Ma, Xiaobing & Peng, Rui & Zhai, Qingqing & Zhao, Yu, 2017. "A preventive maintenance policy based on dependent two-stage deterioration and external shocks," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 201-211.
    14. Zhou, Xiaojun & Wu, Changjie & Li, Yanting & Xi, Lifeng, 2016. "A preventive maintenance model for leased equipment subject to internal degradation and external shock damage," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 1-7.
    15. Gut, Allan & Hüsler, Jürg, 2005. "Realistic variation of shock models," Statistics & Probability Letters, Elsevier, vol. 74(2), pages 187-204, September.
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    Cited by:

    1. Qiu, Qingan & Cui, Lirong, 2019. "Gamma process based optimal mission abort policy," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    2. Qiu, Qingan & Cui, Lirong, 2019. "Optimal mission abort policy for systems subject to random shocks based on virtual age process," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 11-20.
    3. Levitin, Gregory & Xing, Liudong & Luo, Liang, 2019. "Influence of failure propagation on mission abort policy in heterogeneous warm standby systems," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 29-38.

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