延拓小波变换识别的桥梁模态参数研究
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摘要
振动系统模态识别是当今桥梁结构动力特性研究的热点之一。从复模态理论的一般阻尼系统的模态参数分析入手,利用径向神经网络插值技术,对含有噪声的振动信号进行信号预测延拓降噪处理,借助连续的Morlet小波变换,识别出了振动结构系统的模态。以重庆大佛寺长江大桥为研究背景,使用模态叠加法和Morlet小波分析识别结构,二者吻合程度较高。研究结果表明,基于径向神经网络的延拓预测的信号降噪效果好;Morlet小波变换识别模态参数精度满足工程要求。
Modal identification of a vibrating system is one of the research hotspots in studying the structural dynamic characteristics of a bridge.The modal of a vibrating system was identified with the help of continuous Morlet wavelet transform by first analyzing the modal parameters of a general damped system with complex modal analytical theory and then using RBF neural network interpolation technique to predict,extend and denoise the vibration signal containing noise.This paper takes Chongqing Dafosi Yangtze River Bridge as the research object and identifies its vibrating system mode with respectively the mode superposition method and the Morlet wavelet analytical method,of which the results show high conformity.The research findings indicate that signals extended and predicted with RBF neural network can be denoised effectively and the parameter accuracy of modal identification with Morlet wavelet transform meets the engineering requirement.
Modal identification of a vibrating system is one of the research hotspots in studying the structural dynamic characteristics of a bridge.The modal of a vibrating system was identified with the help of continuous Morlet wavelet transform by first analyzing the modal parameters of a general damped system with complex modal analytical theory and then using RBF neural network interpolation technique to predict,extend and denoise the vibration signal containing noise.This paper takes Chongqing Dafosi Yangtze River Bridge as the research object and identifies its vibrating system mode with respectively the mode superposition method and the Morlet wavelet analytical method,of which the results show high conformity.The research findings indicate that signals extended and predicted with RBF neural network can be denoised effectively and the parameter accuracy of modal identification with Morlet wavelet transform meets the engineering requirement.
引文
[1]Bendat J S,Piersol A G.Engineering Application of Correlation andSpectral Analysis[C].Second Edition.New York:John Wiley&Sons,1993.
[2]Brincker R,Zhang L M and Andersen P.Output-Only Modal Anal-ysis by Frequency Domain Decomposition[J].Noise and VibrationEngineering,2001,3:717-723.
[3]Cole H A.On-line failure detection and damping measurement ofaerospace structures by random decrement signatures[R].NASACR-2205,1973.
[4]Overschee P V,Moor B.De.Subspace algorithms for the stochasticidentification problem[J].Automatica,1993,29(3):649-660.
[5]Ibrahim S R.Modal identification techniques assessment and com-parison.Proc.Of 3th IMAC,1985:831-839.
[6]Leuridan J M,Brown D L,Allernang R J.Time domain parameteridentification methods for linear modal analysis:a unifying ap-proach[J].Journal of Vibration,Acostics,stress,and Reliability inDesign,1986,108:1-8.
[7]Juang J N,Pappa R S.An Eigen system Realization Algorithm(ERA)for modal parameter identification and model reduction[J].Guidance,Control,and Dynamics,1985,8(5):620-627.
[8]贺瑞,秦权.改进的NExT-ERA时域模态识别法的误差分析[J].清华大学学报:自然科学版,2009,49(6):787-794.
[9]罗光坤,张令弥.Morlet小波用于环境激励下的模态参数识别研究[J].地震工程与工程振动,2007,27(4):109-115.
[10]Torrence C,Compo G P.A Practical Guide to Wavelet Analysis[J].Bulletin of the American Meteorological Society,1998,79(1):61-78.
[11]Kijewski T,Kareem A.Wavelet Transforms for System Identifica-tion and Associated Processing Concerns[C]//15th ASCE Engi-neering Mechanics Conference.New York:Columbia University,2002:1-8.
[12]伊廷华,李宏南,王国新.基于小波变换的结构模态参数[J].振动工程学报,2006,19(1):51-56.
[13]Suykens J A K,Vandewalle J.Least Squares Support Vector Ma-chine Classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[14]Moody J E,Darken C J.Fast Learning in Networks of Locally-tunedProcessing Units[J].Neural Computation,1989(1):281-294.
[15]朱永,符欲梅,陈伟民.大佛寺长江大桥健康监测系统[J].土木工程学报,2005,38(10):66-71.
[16]赵翔,李爱群,缪长青,等.润扬大桥结构健康监测系统传感器测点布置[J].工业建筑,2005,35(1):82-85.
[2]Brincker R,Zhang L M and Andersen P.Output-Only Modal Anal-ysis by Frequency Domain Decomposition[J].Noise and VibrationEngineering,2001,3:717-723.
[3]Cole H A.On-line failure detection and damping measurement ofaerospace structures by random decrement signatures[R].NASACR-2205,1973.
[4]Overschee P V,Moor B.De.Subspace algorithms for the stochasticidentification problem[J].Automatica,1993,29(3):649-660.
[5]Ibrahim S R.Modal identification techniques assessment and com-parison.Proc.Of 3th IMAC,1985:831-839.
[6]Leuridan J M,Brown D L,Allernang R J.Time domain parameteridentification methods for linear modal analysis:a unifying ap-proach[J].Journal of Vibration,Acostics,stress,and Reliability inDesign,1986,108:1-8.
[7]Juang J N,Pappa R S.An Eigen system Realization Algorithm(ERA)for modal parameter identification and model reduction[J].Guidance,Control,and Dynamics,1985,8(5):620-627.
[8]贺瑞,秦权.改进的NExT-ERA时域模态识别法的误差分析[J].清华大学学报:自然科学版,2009,49(6):787-794.
[9]罗光坤,张令弥.Morlet小波用于环境激励下的模态参数识别研究[J].地震工程与工程振动,2007,27(4):109-115.
[10]Torrence C,Compo G P.A Practical Guide to Wavelet Analysis[J].Bulletin of the American Meteorological Society,1998,79(1):61-78.
[11]Kijewski T,Kareem A.Wavelet Transforms for System Identifica-tion and Associated Processing Concerns[C]//15th ASCE Engi-neering Mechanics Conference.New York:Columbia University,2002:1-8.
[12]伊廷华,李宏南,王国新.基于小波变换的结构模态参数[J].振动工程学报,2006,19(1):51-56.
[13]Suykens J A K,Vandewalle J.Least Squares Support Vector Ma-chine Classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[14]Moody J E,Darken C J.Fast Learning in Networks of Locally-tunedProcessing Units[J].Neural Computation,1989(1):281-294.
[15]朱永,符欲梅,陈伟民.大佛寺长江大桥健康监测系统[J].土木工程学报,2005,38(10):66-71.
[16]赵翔,李爱群,缪长青,等.润扬大桥结构健康监测系统传感器测点布置[J].工业建筑,2005,35(1):82-85.
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