RESEARCH PAPER
DBN-MC Approach for Electronic Safety＆Arming Device PSA Under CCF and Epistemic Uncertainty
1
National Demonstration Center of Experimental Teaching for Ammunition Support and Safety Evaluation Education, Army Engineering University of PLA, China
Submission date: 2026-01-06
Final revision date: 2026-02-12
Acceptance date: 2026-03-01
Online publication date: 2026-03-11
Corresponding author
XiaoDong Zhou
National Demonstration Center of Experimental Teaching for Ammunition Support and Safety Evaluation Education, Army Engineering University of PLA, 050051, shijiazhuang, China
National Demonstration Center of Experimental Teaching for Ammunition Support and Safety Evaluation Education, Army Engineering University of PLA, 050051, shijiazhuang, China
KEYWORDS
Monte CarloElectronic safety and arming deviceDynamic Bayesian NetworkProbabilistic Safety AssessmentCommon-cause FailureEpistemic Uncertainty
TOPICS
ABSTRACT
Electronic safety and arming devices (ESADs) require extremely low unintended-arming probabilities, making success-run demonstration testing impractical. This paper proposes a DBN–MC dynamic PSA framework that encodes sequential enabling constraints and multi-source common-cause failures (CCFs) via local CPTs, and propagates epistemic uncertainty in data-scarce CCF parameters through outer-loop Monte Carlo sampling. In a case study, the mission-end unintended-arming probability is 1.896×10⁻⁷ with a 90% uncertainty interval of [5.421×10⁻⁹, 6.948×10⁻⁷], providing time-dependent risk trajectories with percentile uncertainty band. Decision support is further enabled by intervention-based importance measures—RAW for worst-case amplification and normalized RRW for improvement potential—and by robustness diagnostics that summarize ranking variability across epistemic scenarios. The results show CCF mechanisms dominate worst-case amplification, while improvement priorities are distributed and scenario dependent, supporting uncertainty-informed ESAD design screening and prioritization.
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