EMD-based stochastic subspace identification of structures from operational vibration measurements

Vibration-based structural health monitoring usually needs to extract vibration characteristics from operational vibration measurements. The stochastic subspace identification (SSI) algorithm is an advanced technique for performing such an operational modal analysis. A newly developed signal processing technique, called empirical mode decomposition (EMD), is capable of dealing with non-stationary signals. An EMD-based stochastic subspace identification procedure utilizing operational vibration measurements is presented in this paper. The output-only measurements are first decomposed into modal response functions by means of the EMD technique, on the basis of specified intermittency frequencies. The stochastic subspace identification method is then applied to the decomposed signals to yield the modal parameters. A case study of the operational measurements from a real bridge is presented, in order to illustrate the applicability of the proposed technique. It is demonstrated that the stable pole in the stabilization diagrams becomes unique and the vibration characteristics are easily identified for the decomposed signals, bypassing the influence of other modal components and fake frequencies due to unwanted noise.

[1]  Silian Lin,et al.  Identification of Natural Frequencies and Dampings of In Situ Tall Buildings Using Ambient Wind Vibration Data , 2004 .

[2]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[3]  D. J. Ewins,et al.  Modal Testing: Theory and Practice , 1984 .

[4]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[5]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[6]  Julius S. Bendat,et al.  Engineering Applications of Correlation and Spectral Analysis , 1980 .

[7]  Tong Zhao,et al.  Experimental and Analytical Modal Analysis of Steel Arch Bridge , 2004 .

[8]  H. Saunders Book Reviews : Engineering Applications of Correlatidn and Spectral Analysis: J.S. Bendat and A.G. Piersol John Wiley and Sons, New York, NY, 1980 , 1981 .

[9]  Wei-Xin Ren,et al.  Dynamic and seismic performance of old multi-tiered temples in Nepal , 2003 .

[10]  Jian Chen,et al.  STRUCTURAL DAMAGE DETECTION USING EMPIRICAL MODE DECOMPOSITION: EXPERIMENTAL INVESTIGATION , 2004 .

[11]  B. Peeters,et al.  Reference based stochastic subspace identification in civil engineering , 1999 .

[12]  J. Juang Applied system identification , 1994 .

[13]  Wei-Xin Ren,et al.  Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge , 2005 .

[14]  Issam E. Harik,et al.  Ambient vibration-based seismic evaluation of a continuous girder bridge , 2004 .

[15]  Thomas G. Carne,et al.  The Natural Excitation Technique (NExT) for modal parameter extraction from operating wind turbines , 1993 .

[16]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[17]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[18]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[19]  Wei-Xin Ren,et al.  Dynamic analysis of a half-through concrete-filled steel tubular arch bridge , 2005 .

[20]  W. Ren,et al.  Output-only modal parameter identification of civil engineering structures , 2004 .

[21]  Poul Henning Kirkegaard,et al.  Theory of Covariance Equivalent ARMAV Models of Civil Engineering Structures , 1995 .

[22]  Nuno M. M. Maia,et al.  Theoretical and Experimental Modal Analysis , 1997 .