Optimal mission abort policies for multistate systems

• Published in: Reliability engineering & system safety Vol. 193; p. 106671
• Main Authors: Levitin, Gregory, Finkelstein, Maxim, Huang, Hong-Zong
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.01.2020; Elsevier BV
• Subjects: Binary systems; Mission abort; Mission success probability; Multi-state system; Optimization; Policies; Probability; Reliability engineering; Renewal process of shocks; Survival; System survival probability; System survival probability; Multi-state system; Mission success probability; Mission abort; Renewal process of shocks
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2019.106671

•A multi-state system with variable performance is considered;•The system performs a mission in a random environment modeled by the renewal process of shocks;•The mission is aborted and rescue operation is activated upon occurrence of mth shock;•An algorithm for obtaining mission success and system survival probabilities is presented;•Optimal mission abort strategy balancing these probabilities is obtained. All previous research on optimal mission abort policies was devoted to binary systems that can be only in two states, i.e., operable or failed. This paper considers mission abort and rescue strategies for multistate systems that, apart from a completely operable state and the state of a total failure, can operate in intermediate states with different levels of performance. A system operates in a random environment modeled by a renewal process of shocks. With each shock, the state of a system can deteriorate with certain probabilities that can eventually result in the total failure. Therefore, in order to increase system's survival probability, a mission can be aborted and a rescue procedure can be activated. The trade-off between the mission success probability and the system's survival probability is studied and an optimal number of shocks for initiating the abort procedure is defined by solving the corresponding optimization problem. The detailed numerical example illustrates our findings.