Ensuring the survival of safety-critical systems, such as airplanes and submarines, is of utmost importance, even if it means incomplete task completion. System failures during task execution can have catastrophic consequences. Therefore, prioritizing system survival and temporarily suspending tasks are necessary measures to mitigate the risk of failure. Phase redundancy is a widely employed strategy to enhance the reliability of tasks. By incorporating task redundancy, it becomes possible to successfully accomplish the task even in the event of individual task failures. This study presents a novel condition-based task termination approach that leverages phase redundancy to strike a balance between task success and system survival. Specifically, the proposed approach dynamically terminates tasks based on specific condition information and task progress during each phase. To determine optimal termination thresholds, the study conducts iterative evaluations of task reliability and system survivability. The results demonstrate the effectiveness of the proposed approach in enhancing the probability of system survival by maximizing overall success while prioritizing system safety through active consideration of the system condition and task progress when terminating tasks.
ZhangJYanRZhangY.Reliability analysis of fail-safe systems with heterogeneous and dependent components subject to random shocks. Proc IMechE, Part O: J Risk and Reliability2022; 237(6): 1073–1087. DOI: 10.1177/1748006X221122033.
2.
YangLChenYMaX.A prognosis-centered intelligent maintenance optimization framework under uncertain failure threshold. IEEE Trans Reliab2023. Epub ahead of print 2 August 2023. DOI: 10.1109/TR.2023. 3273082.
3.
MaYZhangMWangM.Analysis of accident risk factors in chemical industry based on ISM-BN. Proc IMechE, Part O: J Risk and Reliability2023. Epub ahead of print 8 November 2023. DOI:10.1177/1748006X231 205382.
4.
QiuQCuiLKongD.Availability and maintenance modeling for a two-component system with dependent failures over a finite time horizon. Proc IMechE, Part O: J Risk and Reliability2019; 233(2): 200–210.
5.
HuangTChenSZhaoY, et al. Reliability assessment of degradation processes with random shocks considering recoverable shock damages. Proc IMechE, Part O: J Risk and Reliability2023; 237(6): 1150–1162.
6.
WangJQiuQWangH.Joint optimization of condition-based and age-based replacement policy and inventory policy for a two-unit series system. Reliab Eng Syst Saf2021; 205: 107251.
7.
QiuQCuiL.Reliability evaluation based on a dependent two-stage failure process with competing failures. Appl Math Model2018; 64: 699–712.
8.
QiuQCuiLGaoH.Availability and maintenance modelling for systems subject to multiple failure modes. Comput Ind Eng2017; 108: 192–198.
9.
QiuQLiuBLinC, et al. Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair. Proc IMechE, Part O: J Risk and Reliability2021; 235(6): 982–997.
10.
LevitinGXingLJohnsonBW, et al. Mission reliability, cost and time for cold standby computing systems with periodic backup. IEEE Trans Comput2015; 64(4): 1043–1057.
11.
FinkelsteinMChaJHLangstonA.Termination versus operation extension for degrading systems. Proc IMechE, Part O: J Risk and Reliability2022; 237(6): 1175–1185. DOI: 10.1177/1748006X221116709.
12.
LevitinGXingLDaiY.Mission abort policy for systems with observable states of standby components. Risk Anal2020; 40(10): 1900–1912.
13.
LevitinGFinkelsteinM.Optimal mission abort policy with multiple shock number thresholds. Proc IMechE, Part O: J Risk and Reliability2018; 232(6): 607–615.
14.
MyersA.Probability of loss assessment of critical k-out-of-n: G systems having a mission abort policy. IEEE Trans Reliab2009; 58(4): 694–701.
15.
YanRZhuXZhuX, et al. Optimal routes and aborting strategies of trucks and drones under random attacks. Reliab Eng Syst Saf2022; 222: 108457.
16.
YangLSunQYeZ-S.Designing mission abort strategies based on early-warning information: application to UAV. IEEE Trans Ind Inform2019; 16(1): 277–287.
17.
ZhaoXDaiYQiuQ, et al. Joint optimization of mission aborts and allocation of standby components considering mission loss. Reliab Eng Syst Saf2022; 225: 108612.
18.
LevitinGFinkelsteinMDaiY.State-based mission abort policies for multistate systems. Reliab Eng Syst Saf, 2020; 204: 107122.
19.
QiuQCuiL.Gamma process based optimal mission abort policy. Reliab Eng Syst Saf2019; 190: 106496.
20.
QiuQCuiL.Optimal mission abort policy for systems subject to random shocks based on virtual age process. Reliab Eng Syst Saf, 2019; 189: 11–20.
21.
QiuQMaillartLMProkopyevOA, et al. Optimal condition-based mission abort decisions. IEEE Trans Reliab, 2023; 72(1): 408–425.
22.
ZhaoXFanYQiuQ, et al. Multi-criteria mission abort policy for systems subject to two-stage degradation process. Eur J Oper Res2021; 295(1): 233–245.
23.
ZhaoXSunJQiuQ, et al. Optimal inspection and mission abort policies for systems subject to degradation. Eur J Oper Res2021; 292(2): 610–621.
24.
YangLChenYQiuQ, et al. Risk control of mission-critical systems: Abort decision-makings integrating health and age conditions. IEEE Trans Ind Inform2022; 18: 6887–6894.
25.
ZhaoXChaiXSunJ, et al. Optimal bivariate mission abort policy for systems operate in random shock environment. Reliab Eng Syst Saf2021; 205: 107244.
QiuQCuiLWuB.Dynamic mission abort policy for systems operating in a controllable environment with self-healing mechanism. Reliab Eng Syst Saf2020; 203: 107069.
28.
LevitinGXingLDaiY.Optimal mission aborting in multistate systems with storage. Reliab Eng Syst Saf2022; 218: 108086.
29.
LevitinGXingLDaiY.Mission aborting and system rescue for multi-state systems with arbitrary structure. Reliab Eng Syst Saf2022; 219: 108225.
30.
LevitinGFinkelsteinMXiangY.Optimal multi-attempt missions with cumulative effect. Reliab Eng Syst Saf2020; 203: 107091.
31.
YangLWeiFQiuQ.Mission risk control via joint optimization of sampling and abort decisions. Risk Anal. Epub ahead of print 26June2023. DOI: 10.1111/risa.14187
32.
ZhaoXChaiXSunJ, et al. Joint optimization of mission abort and component switching policies for multistate warm standby systems. Reliab Eng Syst Saf2021; 212: 107641.
33.
ZhaoXChaiXSunJ, et al. Joint optimization of mission abort and protective device selection policies for multistate systems. Risk Anal, 2022; 42(12): 2823–2834.
34.
ChengGLiLShangguanC, et al. Optimal joint inspection and mission abort policy for a partially observable system. Reliab Eng Syst Saf2023; 229: 108870.
35.
ChengGLiLZhangL, et al. Optimal joint inspection and mission abort policies for degenerative systems. IEEE Trans Reliab2022; 72(1): 137–150.
36.
QiuQKouMChenK, et al. Optimal stopping problems for mission oriented systems considering time redundancy. Reliab Eng Syst Saf2021; 205: 107226.
37.
LevitinGFinkelsteinMHuangH-Z.Optimal abort rules for multiattempt missions. Risk Anal2019; 39(12): 2732–2743.
38.
LevitinGFinkelsteinMXiangY.Optimal mission abort policies for repairable multi-state systems performing multi-attempt mission. Reliab Eng Syst Saf2021; 209: 107497.
39.
LevitinGXingLDaiY.Using kamikaze components in multi-attempt missions with abort option. Reliab Eng Syst Saf2022; 227: 108745.
40.
da Silva RodriguesAJCavalcanteCAVAlbertiAR, et al. Mathematical modelling of mission-abort policies: a review. IMA J Manag Math2023; 34(4): 581–597.
41.
XingL.Reliability evaluation of phased-mission systems with imperfect fault coverage and common-cause failures. IEEE Trans Reliab2007; 56(1): 58–68.
42.
TatikondaMVRosenthalSR.Technology novelty, project complexity, and product development project execution success: a deeper look at task uncertainty in product innovation. IEEE Trans Eng Manag2000; 47(1): 74–87.
43.
WangJWangFWangY, et al. Hytasker: hybrid task allocation in mobile crowd sensing. IEEE Trans Mob Comput2019; 19(3): 598–611.
44.
ZakiRBarabadiABarabadyJ, et al. Observed and unobserved heterogeneity in failure data analysis. Proc IMechE, Part O: J Risk and Reliability2022; 236(1): 194–207.
45.
GaurVYadavOPSoniG, et al. A literature review on network reliability analysis and its engineering applications. Proc IMechE, Part O: J Risk and Reliability2021; 235(2): 167–181.
46.
CuiJCaoLZhangT.A two-stage gaussian process regression model for remaining useful prediction of bearings. Proc IMechE, Part O: J Risk and Reliability. Epub ahead of print 12January2023. DOI: 10.117748006 X221141744.
