신뢰성 데이터를 활용한 유도무기체계 체계안전성 주요안전품목 선정 방법에 대한 연구

Abstract

This study proposes an integrated methodology for the system-safety analysis of unmanned guided weapon systems to identify critical subsystems with high accuracy. Although conventional system safety approaches primarily focused on manned weapon platforms, the increasing complexity and autonomy of guided weapons necessitate a customized analytical framework. Hence, this study combines fault tree analysis (FTA) and failure mode and effects analysis (FMEA) to systematically identify and evaluate potential risk subsystems. FTA is employed to derive top-level accident scenarios based on historical failure cases of similar guided weapons, thus enabling the identification of high-risk subsystems through logical tracing. Subsequently, FMEA is applied to assess component-level failure modes by calculating risk priority numbers based on severity, occurrence, and detection ratings. Qualitative weighting factors—such as mission criticality and strategic impact—are incorporated to enhance the realism and precision of the risk assessment. Beyond subsystem identification, this study integrates reliability, availability, and maintainability (RAM) data to further refine the analysis. By correlating RAM metrics with FMEA and FTA results, this methodology enables the identification of components that pose significant operational safety risks. A composite risk-scoring model that combines quantitative and qualitative factors with RAM-based adjustments is proposed to prioritize critical safety items. This study provides a practical and data-driven framework for enhancing system safety in guided weapon design and operations. Additionally, it offers a foundation for future extensions to autonomous and AI-enabled weapon systems.