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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.9
- 수록면
- 243 - 255 (13page)
- DOI
- 10.33162/JAR.2019.09.19.3.243
이용수
초록· 키워드
오류제보하기
Purpose: Today, prognostics and health management (PHM) is being focused upon in the manufacturing, aerospace, and defense industries. This study aimed to identify the characteristics of diagnosis and PHM, and review the artificial intelligence (AI) techniques used for their implementation.
Methods: PHM involves both diagnosis and prognostics, and AI is used for the diagnosis and PHM of a component or a system. There are two approaches to PHM: model-based and databased approaches. The former involves a system of differential equations, an expert system, a finite state machine, and qualitative reasoning, while the latter involves conventional numerical methods such as linear regression and Kalman filtering, and machine learning methods such as those involving a neural network, a decision tree, or a support vector machine. We review some of these approaches in this paper.
Results: The historical maintenance paradigm is presented along with the evolution of the production paradigm. Diagnosis and PHM are essential for the efficient maintenance of a system, and methods for each of them are briefly described.
Conclusion: A proper understanding of diagnosis and PHM and their appropriate implementation are essential for the efficient maintenance of a system. Model-based and data-based approaches to PHM are briefly described. Both types of approaches have their merits and demerits and they should be tried out.
Methods: PHM involves both diagnosis and prognostics, and AI is used for the diagnosis and PHM of a component or a system. There are two approaches to PHM: model-based and databased approaches. The former involves a system of differential equations, an expert system, a finite state machine, and qualitative reasoning, while the latter involves conventional numerical methods such as linear regression and Kalman filtering, and machine learning methods such as those involving a neural network, a decision tree, or a support vector machine. We review some of these approaches in this paper.
Results: The historical maintenance paradigm is presented along with the evolution of the production paradigm. Diagnosis and PHM are essential for the efficient maintenance of a system, and methods for each of them are briefly described.
Conclusion: A proper understanding of diagnosis and PHM and their appropriate implementation are essential for the efficient maintenance of a system. Model-based and data-based approaches to PHM are briefly described. Both types of approaches have their merits and demerits and they should be tried out.
목차
1. 서론
2. 고장진단(Diagnostics)
3. 고장예지(Prognostics)
4. 건전성관리 알고리즘
5. 결론
References
참고문헌 (40)
참고문헌 신청
Moyne, J. / 2007 / The emergence of industrial control networks for manufacturing control, diagnostics, and safety data / Proceedings of the IEEE 95(1):29~47
Lee, J. / 2011 / Selfmaintenance and engineering immune systems: Towards smarter machines and manufacturing systems / Annual Reviews in Control 35(1):111~122
Djurdjanovic, D. / 2003 / Watchdog agent -an infotronics- based prognostics approach for product performance degradation assessment and prediction / Advanced Engineering Informatics 17(3-4):109~125
Kothamasu, R. / 2006 / System health monitoring and prognostics - a review of current paradigms and practices / The International Journal of Advanced Manufacturing Technology 28(9-10):1012~1024
Takata, S / 2004 / Maintenance: Changing role in life cycle management / CIRP Annals-Manufacturing Technology 53(2):643~655
Lee, J. / 2011 / Selfmaintenance and engineering immune systems: Towards smarter machines and manufacturing systems / Annual Reviews in Control 35(1):111~122
Djurdjanovic, D. / 2003 / Watchdog agent -an infotronics- based prognostics approach for product performance degradation assessment and prediction / Advanced Engineering Informatics 17(3-4):109~125
Kothamasu, R. / 2006 / System health monitoring and prognostics - a review of current paradigms and practices / The International Journal of Advanced Manufacturing Technology 28(9-10):1012~1024
Takata, S / 2004 / Maintenance: Changing role in life cycle management / CIRP Annals-Manufacturing Technology 53(2):643~655
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UCI(KEPA) : I410-ECN-0101-2019-323-001231099