논문 상세보기
Study on Failure Classification of Missile Seekers Using Inspection Data from Production and Manufacturing Phases KCI 등재
생산 및 제조 단계의 검사 데이터를 이용한 유도탄 탐색기의 고장 분류 연구
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/435314
pp.30-39
This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.
2. 관련 문헌 연구
3. 연구방법론
3.1 샘플링
3.2 차원축소 기법
3.3 트리 기반 분류 모델
4. 실험 및 결과
4.1 연구 데이터
4.2 데이터 전처리
4.3 차원축소
4.4 기계학습 모델링
4.5 성능평가
4.6 최적 모형 선정
5. 결론
Acknowledgement
References
