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.
학술저널
Full-text
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Purpose: Failure in thermal power plant generators has high safety and financial risk. Diagnostics and Prognostics to detect abnormality in advance are crucial for failure prevention.
Methods: In this research, fast independent component analysis was applied to select key features from sensor data, and abnormalities were detected when unnatural variation existed in multivariate control charts.
Results: The proposed framework was applied to the dataset acquired from a thermal power plant, and exhibited promising results in detecting and predicting incipient failures.
Conclusion: From the analytical results of an example, it was found that the proposed methodology has potential in failure diagnostics and prognostics to increase the availability of facilities through early detection of incipient failures.
Methods: In this research, fast independent component analysis was applied to select key features from sensor data, and abnormalities were detected when unnatural variation existed in multivariate control charts.
Results: The proposed framework was applied to the dataset acquired from a thermal power plant, and exhibited promising results in detecting and predicting incipient failures.
Conclusion: From the analytical results of an example, it was found that the proposed methodology has potential in failure diagnostics and prognostics to increase the availability of facilities through early detection of incipient failures.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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