An approach to operational modal analysis using the expectation maximization algorithm

Mechanical Systems and Signal Processing

Volumn 31, Issue , 2012, Pages 109-129

  Cara, F Javier ^a   Carpio, Jaime ^a   Juan, Jesús ^a   Alarcón, Enrique ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Expectation Maximization algorithm; Maximum likelihood estimation; Operational modal analysis; Stochastic subspace identification method; Structural dynamics; System identification

Indexed keywords

BENCH-MARK PROBLEMS; BENCHMARK STRUCTURE; DAMPING RATIO; EM ALGORITHMS; EXPECTATION-MAXIMIZATION ALGORITHMS; HIDDEN MODES; HIGH ORDER MODEL; IDENTIFICATION METHOD; INITIAL VALUES; MODAL PARAMETERS; MODE SHAPES; OPERATIONAL MODAL ANALYSIS; OPTIMAL PROPERTIES; PARAMETERS ESTIMATED; RANDOM POINTS; SPURIOUS MODES; STATE SPACE MODEL; STOCHASTIC SUBSPACE IDENTIFICATION; VIBRATION DATA; VIBRATION MODES;

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); MAXIMUM LIKELIHOOD ESTIMATION; SIGNAL RECONSTRUCTION; STATE SPACE METHODS; STOCHASTIC SYSTEMS; STRUCTURAL DYNAMICS;

MODAL ANALYSIS;

EID: 84861625165     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2012.04.004     Document Type: Article

References (18)

1. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc. Ser. B (Methodological) 39 1 1977 1 38
2. R.H. Shumway, and D.S. Stoffer An approach to time series smoothing and forecasting using the EM algorithm J. Time Ser. Anal. 3 4 1982
3. R.H. Shumway, and D.S. Stoffer Time Series Analysis and its Applications 2006 Springer
4. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data second ed. 2002 Wiley New York
5. G.J. McLachlan, and T. Krishnam The EM Algorithm and Extensions 2nd ed. 2008 Wiley New York
6. IASC-ASCE SHM Task Group. Task Group website (last visit on May 2010): 〈 http://bc029049.cityu.edu.hk/asce.shm/wp-home.asp 〉.
7. C. Biernacki, G. Celeux, and G. Govaert Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models Comput. Stat. Data Anal. 41 2003 561 575
8. D. Karlis, and E. Xekelaki Choosing initial values for the EM algorithm for finite mixtures Comput. stat. Data Anal. 41 2003 577 590
9. L. Ljung System Identification. Theory for the Users 2nd ed. 1999 PTR Prentice-Hall Upper Saddle River, NJ
10. H. Akaike Stochastic theory of minimal realization IEEE Trans. Autom. Control 16 6 1974 667 674
11. P. Van Overschee, and B. De Moor Subspace Identification for Linear Systems. Theory - Implementation - Applications 1996 Kluwer Academic Publishers
12. D.E. Newland Mechanical Vibrations Analysis and Computation 2006 Dover Publications
13. B. Peeters, System Identification and Damage Detection in Civil Engineering, Ph.D. Thesis, Katholieke Universiteit Leuven, 2000.
14. E.A. Johnson, H.F. Lam, L.S. Katafygiotis, and J.L. Beck Phase I IASC - ASCE structural health monitoring benchmark problem using simulated data J. Eng. Mech. 130 1 2004 315
15. C.M. Hurvich, and C.L. Tsai Regression and time series model selection in small samples Biometrika 76 1989 297 307
16. J. Ching, and J.L. Beck Bayesian analysis of the Phase II IASC-ASCE structural health monitoring experimental benchmark data J. Eng. Mech. 130 10 2004 1233 1244
17. B. Alicioglu, and M. Lus Ambient vibration analysis with subspace methods and automated mode selection: case studies J. Struct. Eng. ASCE June 2008
18. J. Juang Applied System Identification 1994 Prentice Hall PTR