基于EMD和小波变换的结构风振高阶参振模态识别
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摘要
为了确定结构随机理论求解中的高阶参振模态数目,采用经验模式分解(EMD)与小波变换相结合的方法分析结构气弹模型自激响应数据信号的时-频-谱联合特性,从原始信号中分解出固有模态函数(IMF),再对各个IMF进行小波变换提取信号特征参数,从而识别出结构风振随机计算所需的高阶参振模态截止频率,并将识别结果与直接采用随机理论对不同参振模态的计算结果进行对比验证.结果表明:该方法能够准确地识别出结构风振高阶参振模态,并能在任意敏感的频段捕捉到信号变化的局部特征,能更清晰地刻画信号能量随时间、频率的分布.通过数值模拟和对结构响应的随机理论计算论证了该方法的有效性和实用性.
To identify the number of modes of vibration in the random theory results for wind vibration,the empirical mode decomposition(EMD) method and wavelet transform method are used to analyze the joint characteristic for self-induced response signals of aeroelastic model.First,the intrinsic mode functions(IMF) are directly decomposed from original signal with EMD,and then the signal characteristic parameters of every IMF can be extracted with wavelet transform,which are compared with the results by traditional spectrum analysis method and wavelet transform on original signal.The combination of EMD and wavelet transform method can identify the high-order mode of vibration;the main characteristics of signals in arbitrary frequence band can be obtained;and the distribution of signal energy with time and frequence can be displayed more clearly.The numerical simulation and the analysis of response signal date show that this method is effective and practical.
To identify the number of modes of vibration in the random theory results for wind vibration,the empirical mode decomposition(EMD) method and wavelet transform method are used to analyze the joint characteristic for self-induced response signals of aeroelastic model.First,the intrinsic mode functions(IMF) are directly decomposed from original signal with EMD,and then the signal characteristic parameters of every IMF can be extracted with wavelet transform,which are compared with the results by traditional spectrum analysis method and wavelet transform on original signal.The combination of EMD and wavelet transform method can identify the high-order mode of vibration;the main characteristics of signals in arbitrary frequence band can be obtained;and the distribution of signal energy with time and frequence can be displayed more clearly.The numerical simulation and the analysis of response signal date show that this method is effective and practical.
引文
[1]周暄毅,顾明.上海铁路南站屋盖结构风致抖振响应参数分析[J].同济大学学报,2006,34(5):574-579.
[2]KAREEM A,GURLEY K.Damping in structures:its e-valuation and treatment of uncertainty[J].J of Wind Engand Industrial Aerodynamics,1996,59,131-157.
[3]SONG Yu,XIANG Yiqiang,XU Xing.Mode sharp-based damage identification of bridges[J].Journal of Vi-bration,Measurement&Diagnosis,2005,25(3):111-113.
[4]LI Hui,ZHENG Haiqi,TANG Liwei.Research on faultsdiagnosis of gear wear based on Hilbert-Huang Transform[J].Journal of Vibration,Measurement&Diagnosis,2005,25(3):119-122.
[5]DONOHO D L,JOHNSTONE I M.Ideal spatial adapta-tion by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[6]CHUI C K.An introduction to wavelets[M].New York:Academic Press,1992.
[7]HUANG N E,SHEN Z,LONG S R.The empirical modedecompodition and the Hilbert spectrum for nonlinear andnon-stationary time series analysis[J].Proc R Soc LondA,1998,454:903-995.
[8]HUANG N E,SHEN Z,LONG S R.A new view of non-linear water waves:the hilbert spectrum[J].Annu FluidMech,1999,31:417-457.
[9]柯世堂,赵林,葛耀君.超大型冷却塔结构风振与地震作用影响比较[J].哈尔滨工业大学学报,2010,42(10):1635-1641.
[10]柯世堂,赵林,葛耀君.冷却塔表面脉动风压的非高斯特性及风压极值[C]//第十四届全国结构风工程学术会议论文集.上海:同济大学出版社,2009:354-362.
[11]许林汕,赵林,葛耀君.超大型冷却塔随机风振响应分析[J].振动与冲击,2009,28(4):180-184.
[12]林家浩.随机地震响应的确定性算法[M].北京:中国建筑工业出版社,2000.
[13]林家浩,钟万勰.关于虚拟激励法与结构随机响应的注记[J].计算力学学报,1998,15:217-223.
[2]KAREEM A,GURLEY K.Damping in structures:its e-valuation and treatment of uncertainty[J].J of Wind Engand Industrial Aerodynamics,1996,59,131-157.
[3]SONG Yu,XIANG Yiqiang,XU Xing.Mode sharp-based damage identification of bridges[J].Journal of Vi-bration,Measurement&Diagnosis,2005,25(3):111-113.
[4]LI Hui,ZHENG Haiqi,TANG Liwei.Research on faultsdiagnosis of gear wear based on Hilbert-Huang Transform[J].Journal of Vibration,Measurement&Diagnosis,2005,25(3):119-122.
[5]DONOHO D L,JOHNSTONE I M.Ideal spatial adapta-tion by wavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[6]CHUI C K.An introduction to wavelets[M].New York:Academic Press,1992.
[7]HUANG N E,SHEN Z,LONG S R.The empirical modedecompodition and the Hilbert spectrum for nonlinear andnon-stationary time series analysis[J].Proc R Soc LondA,1998,454:903-995.
[8]HUANG N E,SHEN Z,LONG S R.A new view of non-linear water waves:the hilbert spectrum[J].Annu FluidMech,1999,31:417-457.
[9]柯世堂,赵林,葛耀君.超大型冷却塔结构风振与地震作用影响比较[J].哈尔滨工业大学学报,2010,42(10):1635-1641.
[10]柯世堂,赵林,葛耀君.冷却塔表面脉动风压的非高斯特性及风压极值[C]//第十四届全国结构风工程学术会议论文集.上海:同济大学出版社,2009:354-362.
[11]许林汕,赵林,葛耀君.超大型冷却塔随机风振响应分析[J].振动与冲击,2009,28(4):180-184.
[12]林家浩.随机地震响应的确定性算法[M].北京:中国建筑工业出版社,2000.
[13]林家浩,钟万勰.关于虚拟激励法与结构随机响应的注记[J].计算力学学报,1998,15:217-223.
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