메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Suk Joo Bae (Hanyang University) Jin-ha Tae (Hanyang University) Chang-Sik Chung (Rozetatech) Youngjin Cho (Rozetatech) Hyung-Sool Oh (Kangwon National University) Sun Geu Chae (Hanyang University)
저널정보
한국신뢰성학회 신뢰성응용연구 신뢰성응용연구 제22권 제4호
발행연도
2022.12
수록면
363 - 373 (11page)
DOI
10.33162/JAR.2022.12.22.4.363

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Purpose: Failing power-plant equipment can cause significant harm to both human life and expensive infrastructure. Hence, both effective process-monitoring and failure-prediction techniques are needed to prevent catastrophic equipment failure. Existing time-based maintenance-methods are not enough to prevent catastrophic failures. In this work, a condition-based maintenance (CBM) method, which can predict failures using wavelet-based process-monitoring methods, is studied.
Methods: Both a wavelet spectrum analysis (with coefficients extracted from the wavelet transform) and a T² chart (with a slope and intercept based on a linear trend profile) are used in this study. The results are then embedded into a T² chart.
Results: The proposed methodology in this study is validated using data signals coming from thermoelectric power plants. Overall, the proposed monitoring method has a higher prediction performance using breakdown signals than the traditional T² chart-based maintenance method.
Conclusion: The investigated failure-prediction method is not only more successful but also more specific when abnormal signal patterns are detected before failures. Therefore, using the proposed method for condition-based maintenance can help save resources and prevent human losses.

목차

1. Introduction
2. Wavelet Spectrum Analysis
3. T² Chart on the Monitoring of Linear Profile
4. Application to a Generator in a Power Plant
5. Conclusions and Future Research
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0