Mission Abort Policy for Systems with Observable States of Standby Components

Abstract

For some critical applications, successfully accomplishing the mission or surviving the system through aborting the mission and performing a rescue procedure in the event of certain deterioration condition being satisfied are both pivotal. This has motivated considerable studies on mission abort policies (MAPs) to mitigate the risk of system loss in the past several years, especially for standby systems that use one or multiple standby sparing components to continue the mission when the online component fails, improving the mission success probability. The existing MAPs are mainly based on the number of failed online components ignoring the status of the standby components. This article makes contributions by modeling standby systems subject to MAPs that depend not only on the number of failed online components but also on the number of available standby components remaining. Further, dynamic MAPs considering another additional factor, the time elapsed from the mission beginning in the event of the mission abort decision making, are investigated. The solution methodology encompasses an event-transition based numerical algorithm for evaluating the mission success probability and system survival probability of standby systems subject to the considered MAPs. Examples are provided to demonstrate the benefit of considering the state of standby components and elapsed operation time in obtaining more flexible MAPs.

Mission risk control via joint optimization of sampling and abort decisions

“…It is worth noting that in most existing condition‐based mission abort models, assume that the system state is fully observable via condition monitoring (Levitin, Xing, et al., 2020; Levitin et al., 2021b; Yang et al., 2022; Peng et al., 2010; Zhao et al., 2022). The mission abort decision‐making problem in case of partial condition monitoring information is studied in only a few recent works (Cheng et al., 2022).…”

Mission abort strategy for generalized k-out-of-n: F systems considering competing failure criteria

“…A mission abort policy which depends on the number of failed components and the number of remaining available standby components was studied. 30 Despite the significant theoretical advancement in the modeling and optimization of mission abort policies, little work has considered the abort policy for multi-component systems whose failure behavior is dependent on the position of failed components, for example the m-consecutive-k c -out-of-n: F system. This system breaks down if and only if there are at least m non-overlapping runs of k c consecutive failed components.…”

Joint optimization of mission abort and protective device selection policies for multistate systems

“…(2020a) considered a case when a mission consists of several independent subtasks that can be performed by a heterogeneous set of independent units and obtained the optimal subtask abort policy for each unit. These mission abort policies were further extended to standby systems to find the optimal combination of mission abort and standby components arrangement policies (Levitin et al., 2018a; Levitin et al., 2019; Levitin et al., 2020). Levitin et al.…”

“…The mission will be aborted once the condition of the system is worse than a control threshold. As far as we know, there are two mainly used approaches in the published literatures for risk analysis (such as evaluating the reliability indices): probability derivation (Levitin & Finkelstein, 2018b; Levitin et al., 2019) and event‐transition based numerical algorithm (Levitin et al., 2020). Based on the established model, mission reliability and system survivability are evaluated by using the Markov process imbedding method (MPIM) (Gao et al., 2019; Wu et al., 2021).…”