Reference-based combined deterministic–stochastic subspace identification for experimental and operational modal analysis

In this paper, the use of the combined deterministic-stochastic subspace identification algorithm for the experimental modal analysis of mechanical structures is discussed. The algorithm requires artificial forces to be applied to the structure, so it can be used for experimental modal analysis (EMA). The algorithm can also be used for operational modal analysis (OMA), since the excitation level of the artificial force(s) can be low compared to the excitation level of the ambient forces. Both the modes that are artificially excited and those that are excited by the ambient forces are identified. This type of operational modal analysis is called an OMAX analysis (Operational Modal Analysis with eXogenous inputs) [1]. The main advantages of OMAX over OMA are that the modes that are excited by the artificial forces can be scaled to unity modal mass and that a higher number of modes can be identified.

[1]  B. Moor,et al.  Model reduction and energy analysis as a tool to detect spurious modes , 2002 .

[2]  E. Parloo,et al.  SENSITIVITY-BASED OPERATIONAL MODE SHAPE NORMALISATION , 2002 .

[3]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[4]  K. Bathe.  Finite Element Procedures , 1995 .

[5]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[6]  Paul Sas,et al.  Modal Analysis Theory and Testing , 2005 .

[7]  Joe Brewer,et al.  Kronecker products and matrix calculus in system theory , 1978 .

[8]  Guido De Roeck,et al.  REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS , 1999 .

[9]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

[10]  Richard Evelyn Donohue Bishop,et al.  An investigation into the theory of resonance testing , 1963, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[11]  G. Roeck,et al.  Structural damage identification of the highway bridge Z24 by FE model updating , 2004 .

[12]  Charles C. Kennedy,et al.  Use of Vectors in Vibration Measurement and Analysis , 1947 .

[13]  J. Z. Zhu,et al.  The finite element method , 1977 .

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

[15]  R. Pintelon.  System identification , 2001 .

[16]  E. Parloo,et al.  Combined Deterministic-Stochastic Frequency-Domain Subspace Identification for Experimental and Operational Modal Analysis , 2004 .

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

[18]  E. Parloo,et al.  Sensitivity-based operational mode shape normalisation: Application to a bridge , 2005 .

[19]  G. De Roeck,et al.  Z24 bridge damage detection tests , 1999 .

[20]  Bart Peeters,et al.  COMPARATIVE STUDY OF MODAL ANALYSIS TECHNIQUES FOR BRIDGE DYNAMIC CHARACTERISTICS , 2003 .

[21]  Edward R. Dougherty,et al.  Random processes for image and signal processing , 1998, SPIE / IEEE series on imaging science and engineering.

[22]  Masanao Aoki,et al.  State Space Modeling of Time Series , 1987 .

[23]  Bart Peeters,et al.  System identification and damage detection in civil engineering , 2000 .

[24]  Guido De Roeck,et al.  The state‐of‐the‐art of damage detection by vibration monitoring: the SIMCES experience , 2003 .