인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Purpose: This study evaluates the applicability of reliability growth slope-based extrapolation for predicting future MTBF using cumulative aircraft operational data.
Methods: Operational data from a Republic of Korea Air Force aircraft, including cumulative flight hours and failure records, were analyzed using the Duane reliability growth model. Reliability growth slopes were estimated at 10,000, 30,000, and 50,000 flight-hour reference points using a sliding window approach. The estimated slopes were extrapolated to predict future MTBF values at later operational stages. Prediction accuracy was evaluated by comparing predicted and actual MTBF values using RMSE.
Results: MTBF predictions based on early operational data showed relatively higher error rates.
Prediction accuracy improved as cumulative operational data increased. These results indicate that reliability growth slope estimation is highly dependent on the sufficiency of operational data.
Conclusion: This study demonstrates the applicability of reliability growth analysis using operational aircraft maintenance data. The results show that operational reliability data can support MTBF forecasting and logistics decision-making. Prediction accuracy improved as operational data accumulated over time. Future research should incorporate subsystem-level analysis and additional operational factors.
Methods: Operational data from a Republic of Korea Air Force aircraft, including cumulative flight hours and failure records, were analyzed using the Duane reliability growth model. Reliability growth slopes were estimated at 10,000, 30,000, and 50,000 flight-hour reference points using a sliding window approach. The estimated slopes were extrapolated to predict future MTBF values at later operational stages. Prediction accuracy was evaluated by comparing predicted and actual MTBF values using RMSE.
Results: MTBF predictions based on early operational data showed relatively higher error rates.
Prediction accuracy improved as cumulative operational data increased. These results indicate that reliability growth slope estimation is highly dependent on the sufficiency of operational data.
Conclusion: This study demonstrates the applicability of reliability growth analysis using operational aircraft maintenance data. The results show that operational reliability data can support MTBF forecasting and logistics decision-making. Prediction accuracy improved as operational data accumulated over time. Future research should incorporate subsystem-level analysis and additional operational factors.
본문·목차
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