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논문 기본 정보

자료유형
학술저널
저자정보
Min Gyu Jeon (Korea Maritime and Ocean Univ.) Deog Hee Doh (Korea Maritime and Ocean Univ.) Ue Kan Kim (Korea Maritime and Ocean Univ.) Kang Ki Kim (Korea Maritime and Ocean Univ.)
저널정보
한국마린엔지니어링학회 Journal of Advanced Marine Engineering and Technology (JAMET) 한국마린엔지니어링학회지 제38권 제10호
발행연도
2014.12
수록면
1,275 - 1,280 (6page)

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초록· 키워드

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In this study, the performance of a real-time micro telescopic monitoring system is evaluated, in which an artificial neural network is adopted for the diagnoses of vibratory bodies, such as solid piping system or machinery. The structural vibration was measured by a non-contact remote sensing method, in which images of a high-speed high-definition camera were used. The structural vibration data that can be obtained by the PIV (particle image velocimetry) technique were used for training the neural network. The structures of the neural network are dynamically changed and their performances are evaluated for the constructed diagnosis system. Optimized structures of the neural network are proposed for real-time diagnosis for the piping system. It was experimentally verified that the performances of the neural network used for real-time monitoring are influenced by the types of the vibration data, such as minimum, maximum and average values of the vibration data. It concludes that the time-mean values are most appropriate for monitoring the piping system.

목차

Abstract
1. Introduction
2. Measurement System
3. Results and Discussions
4. Conclusions
References

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