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Automated FMEA Framework Reflecting Field Failures Using Local LLM-Based RAG KCI 등재
로컬 LLM 기반 RAG를 활용한 고장이력 반영 FMEA 자동화 프레임워크
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/445030
pp.93-103
This study addresses the limitations of traditional Failure Modes and Effects Analysis (FMEA), which heavily relies on expert judgment and lacks the ability to effectively incorporate unstructured failure history data such as warranty claims and maintenance records into the design stage. To overcome these challenges, we propose an automated FMEA framework based on a Retrieval-Augmented Generation (RAG) architecture integrated with a Local Large Language Model (LLM) in a secure, locally managed environment. The proposed system stores claim and test data in a vector database and leverages the LLM to retrieve and analyze relevant information, enabling automatic extraction of new failure modes and dynamic updates to FMEA documents. Additionally, the system recalculates Risk Priority Number (RPN) by adjusting severity, occurrence, and detection ratings when recurring failures are detected. To improve response quality, we applied prompt engineering and optimized chunking parameters during data retrieval. This research demonstrates the feasibility of achieving a life cycle-integrated quality enhancement framework throughout the product lifecycle while ensuring data security.
2. 관련 문헌 연구
3. Methodology
3.1 사용 모델
3.2 RAG(Retrieval-Augmented Generation)
3.3 FMEA 자동화 프레임워크
4. 실 험
4.1 실험 데이터
4.2 실험 구성
4.3 실험 방법
4.4 실험 결과
5. 결 론
Acknowledgement
References
