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

SUMMARY A new damage indicator denoted by the distance between ARMA models is proposed in this paper to identify structural damage including its location and severity. Two definitions are introduced as the distance, either the cepstral metric or subspace angles of ARMA models. However, the accuracy is deteriorated when the multiple inputs have strong correlations. To overcome this difficulty, a pre-whitening filter is applied. Thus, the proposed damage indicator is applicable for varieties of excitation types in civil engineering, such as wind, traffic loading and earthquake excitations. A five-storey building model is used for performance verification when subjected to different excitations. Ambient force and earthquake input have been used as excitations acting on the structure. Two calculating methods of the proposed damage indicators are both evaluated. When the excitations are mutually correlated, by using the pre-whitening filter, the damage identification ability of the proposed damage indicator improves significantly, especially for damage localization. The damage indicator increases monotonically with damage severity, which provides the potential for damage quantification. Copyright # 2007 John Wiley & Sons, Ltd.

[1]  W. Gersch,et al.  Synthesis of multivariate random vibration systems: A two-stage least squares AR-MA model approach☆ , 1977 .

[2]  B. P. Bogert,et al.  The quefrency analysis of time series for echoes : cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking , 1963 .

[3]  Paul J. Fanning,et al.  Auto-Regression and Statistical Process Control Techniques Applied to Damage Indication in Telecommunication Masts , 2001 .

[4]  Ashutosh Bagchi,et al.  Performance of Vibration-based Techniques for the Identification of Structural Damage , 2006 .

[5]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[6]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[7]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[8]  Richard J. Martin.  A metric for ARMA processes , 2000, IEEE Trans. Signal Process..

[9]  Akira Mita.  Structural dynamics for health monitoring , 2003 .

[10]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[11]  Sudhakar M. Pandit,et al.  Statistical moments of autoregressive model residuals for damage localisation , 2006 .

[12]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[13]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[14]  K. Law,et al.  Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure , 2006 .

[15]  Yong-Lin Pi,et al.  Modal Identification of Vibrating Structures Using ARMA Model , 1989 .

[16]  Bart De Moor,et al.  Subspace angles between ARMA models , 2002, Syst. Control. Lett..