Damage indicator defined as the distance between ARMA models for structural health monitoring

Structural Control and Health Monitoring

Volumn 15, Issue 7, 2008, Pages 992-1005

  Haitao, Li ^a   Mita, Akira ^a  

^a KEIO UNIVERSITY   (Japan)

Author keywords

ARMA model; Cepstral metric; Damage indicator; Pre whitening filter; Subspace angle

Indexed keywords

CIVIL ENGINEERING; EARTHQUAKES; SEISMOLOGY; STRUCTURAL ANALYSIS; STRUCTURAL HEALTH MONITORING; TRAFFIC SURVEYS; WAVE FILTERS;

ARMA MODEL; CEPSTRAL METRIC; DAMAGE INDICATOR; PRE-WHITENING FILTER; SUBSPACE ANGLE;

DAMAGE DETECTION;

EID: 55749083864     PISSN: 15452255     EISSN: 15452263     Source Type: Journal    
DOI: 10.1002/stc.235     Document Type: Article

References (16)

1. Doebling SW, Farrar CR, Prime MB, Shevitz DW. A summary review of vibration-based damage identification methods. Shock and Vibration Digest 1998; 30(2):91-105.
2. Humar J, Bagchi A, Xu H. Performance of vibration-based techniques for the identification of structural damage. Structural Health Monitoring 2006; 5(3):215-241.
3. Carden EP, Fanning P. Vibration based condition monitoring: a review, Structural Health Monitoring 2004; 3(4):355-377.
4. Fanning P, Carden EP. Auto-regressive and statistical process control techniques applied to damage indication in telecommunication masts. Key Engineering Materials 2001; 204-205:251-260.
5. Sohn H, Farrar CR. Damage diagnosis using time series analysis of vibration signals. Smart Materials and Structures 2001; 10:446-451.
6. Mattson SG, Pandit SM. Statistical moments of autoregressive model residuals for damage localisation. Mechanical Systems and Signal Processing 2006; 20(3):627-645.
7. Nair KK, Kiremidjian AS, Law KH. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration 2006; 291(1-2):349-368.
8. Martin RJ. A metric for ARMA processes. IEEE Transactions on Signal Processing 2000; 48(4):1164-1170.
9. Van Overschee P, De Moor B. Subspace algorithms for the stochastic identification problem. Automatica 1993; 29:649-660.
10. De Cock K, De Moor B. Subspace angles between ARMA models. Systems and Control Letters 2002; 46(4):265-270.
11. Mita A. Structural Dynamics for Health Monitoring. Sankeisha: Japan, 2003; 77-96.
12. Gersch W, Nielsen NN, Akaite H. Synthesis of multivariate random vibration systems: a two-stage least squares AR-MA model approach. Journal of Sound and Vibration 1977; 52(4):553-565.
13. Pi YL, Mickleborough NC. Modal identification of vibrating structures using ARMA model. Journal of Engineering Mechanics 1989; 115(10):2232-2249.
14. Bogert BP, Healy MJR, Tukey JW. The quefrency analysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking. Proceedings of the Symposium on Time Series Analysis, Rosenblatt M (ed.). Wiley: New York, 1963; 209-243.
15. Oppenheim AV, Schafer RW. Digital Signal Processing, Prentice-Hall: London, 1975; 480-531.
16. Cichocki A, Amari S. Adaptive Blind Signal and Image Processing. Wiley: West Sussex, 2002; 130-175.