Adaptive Mission Abort Planning Integrating Bayesian Parameter Learning

Yuhan Ma, Fanping Wei, Xiaobing Ma, Qingan Qiu () and Li Yang ()
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Yuhan Ma: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Fanping Wei: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Xiaobing Ma: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Qingan Qiu: School of Management, Beijing Institute of Technology, Beijing 100081, China
Li Yang: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Mathematics, 2024, vol. 12, issue 16, 1-19

Abstract: Failure of a safety-critical system during mission execution can result in significant financial losses. Implementing mission abort policies is an effective strategy to mitigate the system failure risk. This research delves into systems that are subject to cumulative shock degradation, considering uncertainties in shock damage. To account for the varied degradation parameters, we employ a dynamic Bayesian learning method using real-time sensor data for accurate degradation estimation. Our primary focus is on modeling the mission abort policy with an integrated parameter learning approach within the framework of a finite-horizon Markov decision process. The key objective is to minimize the expected costs related to routine inspections, system failures, and mission disruptions. Through an examination of the structural aspects of the value function, we establish the presence and monotonicity of optimal mission abort thresholds, thereby shaping the optimal policy into a controlled limit strategy. Additionally, we delve into the relationship between optimal thresholds and cost parameters to discern their behavior patterns. Through a series of numerical experiments, we showcase the superior performance of the optimal policy in mitigating losses compared with traditional heuristic methods.

Keywords: risk control; mission reliability assessment; Bayesian learning; risk management; survivability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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