Joint optimization of mission aborts and allocation of standby components considering mission loss

Xian Zhao, Ying Dai, Qingan Qiu and Yaguang Wu

Reliability Engineering and System Safety, 2022, vol. 225, issue C

Abstract: Mission abort is an effective way to enhance system safety during mission execution. Existing multi-attempt mission abort models can be divided into two main categories according to the additivity of completed missions: non-accumulative models and completely cumulative models. This paper studies the optimal mission abort and allocation of standby components policies for the k-out-of-(n+m):F system considering partial mission loss. During each attempt, the mission abort decision is dynamically controlled via predetermined abort thresholds and the rescue procedure (RP) starts immediately upon mission abort. In most studies, after a successful RP, the system is commonly restored to an ‘as good as new’ state with the underlying assumption that the standby components are always adequate. However, due to factors such as cost and capacity, the number of standby components may be limited. This paper proposes a dynamic allocation policy of a fixed number of standby components. The aim is to determine the optimal number of the failed components be replaced after each RP. By using a recursive algorithm, mission reliability and system survivability are derived. The objective is to minimize the expected cost and balance the mission reliability and the system survivability. The advantage of the proposed policy is justified by the policy comparison. Finally, the obtained results are demonstrated considering an autonomous underwater vehicle performing a photography mission.

Keywords: Mission abort; Partial replacement; Allocation of standby components; Mission reliability; System survivability (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002551

DOI: 10.1016/j.ress.2022.108612

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