基于HHT的多自由度结构渐进损伤特征提取方法
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摘要
大型复杂工程结构的损伤实际上是一个渐进损伤的过程,为解决结构损伤识别中非平稳随机信号的时变性并有效地识别这个损伤过程,研究了基于Hilbert-Huang变换的结构渐进损伤特征提取方法.首先模拟产生了多自由度结构系统发生渐进损伤的加速度振动信号;然后对加速度振动信号进行经验模式分解,将其分解为多个平稳的固有模式函数之和;再选取若干个包含主要损伤信息的固有模式函数进行Hilbert变换,提取瞬时频率作为特征参数进行损伤特征提取.研究结果表明:HHT是一种有效的信号处理方法,通过提取瞬时频率,可以准确地提取结构渐进损伤的特征.
The large complex structure damage is a progressive process actually.In order to solve the time-variable of non-steady random signal during the process of structure health monitoring and to identify the process efficiently,a feature extraction method of structure progressive damage is studied based on Hilbert-Huang transform.First,the acceleration vibration signals of a multiple-degree of freedom structure model are simulated by reducing the stiffness gradually,and decomposed several smoothed intrinsic mode function(IMF).Then,some IMFs which contain main information are selected and transformed by Hilbert,and the instantaneous frequency is extracted as feature parameter.The result indicated that:HHT is an effective signal processing way that can accurately extract progressive damage feature by means of extracting instantaneous frequency.
The large complex structure damage is a progressive process actually.In order to solve the time-variable of non-steady random signal during the process of structure health monitoring and to identify the process efficiently,a feature extraction method of structure progressive damage is studied based on Hilbert-Huang transform.First,the acceleration vibration signals of a multiple-degree of freedom structure model are simulated by reducing the stiffness gradually,and decomposed several smoothed intrinsic mode function(IMF).Then,some IMFs which contain main information are selected and transformed by Hilbert,and the instantaneous frequency is extracted as feature parameter.The result indicated that:HHT is an effective signal processing way that can accurately extract progressive damage feature by means of extracting instantaneous frequency.
引文
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[10]http:∥cive.seas.wustl.edu/wusceel/ASCE.shm/experim-phase-2.ht m
[2]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and Hilbert spectrumfor nonlinear and non-stationary time series analysis[J].Proc R Soc Lond A,1998,454:903-995.
[3]LI UJ B,WANG X W,YUAN S F.On Hilbert-Huang Transform Approach for Structural Health Monitoring[J].Journal of Intelligent Material Systems and Structures,2006,17(8/9):721-728.
[4]XU Y L,CHEN J.Structural damage detection using empirical mode decomposition:experimental investigation[J].Journal of engineering mechanics,2004,10:1279-1288.
[5]PINES D,SALVINO L M.Structural health monitoring using empirical mode decomposition and the Hilbert phase[J].Journal of Sound and Vibration,2006,294:97-124.
[6]公茂盛,谢礼立.结构地震反应记录HHT分析[J].西安建筑科技大学学报:自然科学版,2006,38(4):450-454.GONG Mao-sheng,XIE Li-li.HHTanalysis of structural earthquake response records[J].J.Xi′an Univ.of Arch.&Tech.(Natural Science Edition),2006,38(4):450-454.
[7]刘霖雯,刘超,江成顺.EMD新算法及其应用[J].系统仿真学报,2007,19(2):446-447.LI U Lin-wen,LI U Chao,JIANG Cheng-shun.Novel EMD algorithm and its application[J].Journal of System Simulation,2007,19(2):446-447.
[8]周云,陈存恩,黄镇梁,等.土木工程减灾防灾学[M].广州:华南理工大学出版社,2002.ZHOU Yun,CHEN Cun-en,HUANG Zhen-liang,et al.Disaster prevention and mitigation of Civil Engineering[M].Guangzhou:South China University of Technology Press,2002.
[9]王济,胡晓.MATLAB在振动信号处理中的应用[M].北京:中国水利水电出版社,2006.WANGJi,HU Xiao.The application of MATLABin the vibration Signal Processing[M].Beijing:China Water Conservancy and Hydropower Press,2006.
[10]http:∥cive.seas.wustl.edu/wusceel/ASCE.shm/experim-phase-2.ht m
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