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학술논문한국소음진동공학회논문집2005.02 발행

경험 모드 분리법을 이용한 감쇠 진동 신호의 분석

Analysis of Damped Vibration Signal Using Empirical Mode Decomposition Method

이인재(한국과학기술연구원); 이종민(한국과학기술연구원); 황요하(한국과학기술연구원); 허건수(한양대학교)

15권 2호, 192~198쪽

초록

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

Abstract

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

발행기관:
한국소음진동공학회
분류:
기계공학

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경험 모드 분리법을 이용한 감쇠 진동 신호의 분석 | 한국소음진동공학회논문집 2005 | AskLaw | 애스크로 AI