인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 553 - 561 (9page)
이용수
초록· 키워드
To easily understand and systematically express the behaviors of the industrial systems, various systemmodeling techniques have been developed. Particularly, the importance of system modeling has beengreatly emphasized in recent years since modern industrial systems have become larger and morecomplex. Multilevel flow modeling (MFM) is one of the qualitative modeling techniques, applied for the representationand reasoning of target system characteristics and phenomena. MFM can be applied to industrialsystems without additional domain-specific assumptions or detailed knowledge, and qualitativereasoning regarding event causes and consequences can be conducted with high speed and fidelity. However, current MFM techniques have a limitation, i.e., the dynamic features of a target system arenot considered because time-related concepts are not involved. The applicability of MFM has beenrestricted since time-related information is essential for the modeling of dynamic systems. Specifically,the results from the reasoning processes include relatively less information because they did not utilizetime-related data. In this article, the concepts of time-to-detect and time-to-effect were adopted from the system failuremodel to incorporate time-related issues into MFM, and a methodology for enhancing MFM-basedreasoning with time-series data was suggested.
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