This paper is intended to point out the relationship among current time domain modal analysis methods by employing generalized eigenvalue decomposition. Ibrahim time domain (ITD), least-squares complex exponential (LSCE) and eigensystem realization algorithm (ERA) methods are reviewed and chosen to do the comparison. Reformulation to their original forms shows these three methods can all be attributed to a generalized eigenvalue problem with different matrix pairs. With this general format, we can see that single-input multioutput (SIMO) methods can easily be extended to multi-input multioutput (MIMO) cases by taking advantage of a generalized Hankel matrix or a generalized Toeplitz matrix.
1.
Mendes
, M.
, and Silva
, J. M.
, 1997, Theoretical and Experimental Modal Analysis
, Research Studies Press
, Cambridge, UK.2.
Brown
, D. L.
, Allemang
, R. J.
, Zimmerman
, R.
, and Mergeay
, M.
, 1979, “Parameter Estimation Techniques for Modal Analysis
,” SAE Technical Paper Series, 790221, pp. 15
–24
.3.
Ibrahim
, S. R.
, and Mikulcik
, E. C.
, 1973, “A Time Domain Modal Vibration Test Technique
,” Shock Vib. Bull.
, 43
(4
), pp. 21
–37
.4.
Ibrahim
, S. R.
, and Mikulcik
, E. C.
, 1977, “A Method for the Direct Identification of Vibration Parameters From the Free Response
,” Shock Vib. Bull.
, 47
(4
), pp. 183
–198
.5.
Ibrahim
, S. R.
, 1978, “Modal Confidence Factor in Vibration Testing
,” Shock Vib. Bull.
, 48
(1
), pp. 65
–75
.6.
Pappa
, S. R.
, and Ibrahim
, S. R.
, 1981, “A Parameter Study of the Ibrahim Time Domain Identification Algorithm
,” Shock Vib. Bull.
, 51
(3
), pp. 43
–73
.7.
Vold
, H.
, Kundrat
, J.
, Rocklin
, G. T.
, and Russel
, R.
, 1982, “A Multi-Input Modal Estimation Algorithm for Mini-Computers
,” SAE Technical Paper Series, 820194, pp. 828
–841
.8.
Juang
, J. N.
, and Pappa
, R. S.
, 1985, “An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction
,” J. Guid. Control Dyn.
0731-5090, 8
(5
), pp. 620
–627
.9.
Juang
, J. N.
, and Pappa
, R. S.
, 1986, “Effects of Noise on Modal Parameters Identified by the Eigensystem Realization Algorithm
,” J. Guid. Control Dyn.
0731-5090, 9
(3
), pp. 294
–303
.10.
Peeters
, B.
, and Roeck
, G. D.
, 1999, “Reference-Based Stochastic Subspace Identification for Output-Only Modal Analysis
,” Mech. Syst. Signal Process.
0888-3270, 13
(6
), pp. 855
–878
.11.
Overschee
, P. V.
, and Moor
, B. D.
, 1993, “Subspace Algorithm for the Stochastic Identification Problem
,” Automatica
0005-1098, 29
(3
), pp. 649
–660
.12.
Benveniste
, A.
, and Fuchs
, J. J.
, 1985, “Single Sample Modal Identification of a Nonstationary Stochastic Process
,” IEEE Trans. Autom. Control
0018-9286, 30
(1
), pp. 66
–74
.13.
Allemang
, R. J.
, and Brown
, D. L.
, 1998, “A Unified Matrix Polynomial Approach to Modal Identification
,” J. Sound Vib.
0022-460X, 211
(3
), pp. 301
–322
.14.
Chelidze
, D.
, and Zhou
, W.
, 2006, “Smooth Orthogonal Decomposition Based Vibration Mode Identification
,” J. Sound Vib.
0022-460X, 292
(3-5
), pp. 461
–476
.15.
Tong
, L.
, Liu
, R. W.
, Soon
, V. C.
, and Huang
, Y. F.
, 1991, “Indeterminacy and Identifiability of Blind Identification
,” IEEE Trans. Circuits Syst.
0098-4094, 38
(5
), pp. 499
–509
.16.
Belouchrani
, A.
, Abed-Meraim
, K.
, Cardoso
, J. F.
, and Moulines
, E.
, 1997, “A Blind Source Separation Technique Using Second-Order Statistics
,” IEEE Trans. Signal Process.
1053-587X, 45
(2
), pp. 434
–444
.17.
Zhou
, W.
, and Chelidze
, D.
, 2007, “Blind Source Separation Based Vibration Mode Identification
,” Mech. Syst. Signal Process.
0888-3270, doi: (to be published).Copyright © 2008
by American Society of Mechanical Engineers
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