Online damage detection using pair cointegration method of time-varying displacement

Environmental and operational variables are inevitable concerns by researchers and engineers when implementing the damage detection algorithm in practical projects, because the change of structural behavior could be masked by the conditions in a large extent. Thus, reliable damage detection methods should have a virtue of immunity from environmental and operational variables. In this paper, the pair cointegration method was presented as a novel way to remove the effect of environmental variables. At the beginning, the concept and procedure of this approach were introduced, and then the theoretical formulation and numerical simulations were put forward to illustrate the feasibility. The jump exceeding the control limit in the residual indicates the occurrence of damage, while the direction and magnitude imply the most potential damage location. In addition, the simulation results show that the proposed method has strong ability to resist the noise.

[1]  Chin-Hsiung Loh,et al.  Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam , 2011 .

[2]  Hoon Sohn,et al.  Effects of environmental and operational variability on structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[3]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[4]  Maria Q. Feng,et al.  Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration , 2003 .

[5]  Andrew Y. T. Leung,et al.  Cointegration Testing Method for Monitoring Nonstationary Processes , 2009 .

[6]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

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

[8]  S. Johansen.  STATISTICAL ANALYSIS OF COINTEGRATION VECTORS , 1988 .

[9]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[10]  Eun-Taik Lee,et al.  Damage detection of damaged beam by constrained displacement curvature , 2008 .

[11]  Charles R. Farrar,et al.  Machine learning algorithms for damage detection under operational and environmental variability , 2011 .

[12]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[13]  Charles W. Roeder,et al.  Temperature Dependent Bridge Movements , 1992 .

[14]  Gaëtan Kerschen,et al.  Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis , 2005 .

[15]  Jm M. Ko,et al.  Eliminating Temperature Effect in Vibration-Based Structural Damage Detection , 2011 .

[16]  Keith Worden,et al.  Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data , 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.