Mission Abort Policy for Systems with Observable States of Standby Components

Gregory Levitin; Liudong Xing; Yuanshun Dai

doi:10.1111/risa.13532

Mission Abort Policy for Systems with Observable States of Standby Components

Risk Anal. 2020 Oct;40(10):1900-1912. doi: 10.1111/risa.13532. Epub 2020 Jul 11.

Authors

Gregory Levitin^{1

2}, Liudong Xing³, Yuanshun Dai¹

Affiliations

¹ Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China, Chengdu, China.
² The Israel Electric Corporation, P. O. Box 10, Haifa, 31000, Israel.
³ University of Massachusetts, Dartmouth, MA, 02747, USA.

PMID: 32654208
DOI: 10.1111/risa.13532

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.

Keywords: Dynamic policy; mission abort policy (MAP); mission success probability; rescue; standby component; system survival probability.