Damage Detection and Damage Detectability—Analysis and Experiments

A technique to identify structural damage in real time using limited instrumentation is presented. Contrast maximization is used to find the excitation forces that create maximum differences in the response of the damaged structure and the analytical response of the undamaged structure. The optimal excitations for the damaged structure are then matched against a database of optimal excitations to locate the damage. To increase the reliability of the approach under modeling and measurement errors, the contrast maximization approach is combined with an approach based on changes in frequency signature. The detectability of any particular damage with the proposed technique depends on the ratio of the magnitude of damage and the magnitude of errors in the measurements, as well as on how much the damage influences the measurements. A damage detectability prediction measure, that incorporates these effects, is developed. The technique is first tested numerically on a 36 degree-of-freedom space truss. To simulate experimental conditions, an extensive study is carried out in the presence of noise. A similar truss is then built and the finite-element method (FEM) model of the structure is corrected using experimental data. The technique is applied to locate the damage in several members. The experimental results indicate that this technique can robustly identify the damaged member with limited measurements and real-time computation. The effectiveness of the damage detection measure is also demonstrated.

[1]  David C. Zimmerman,et al.  Correcting finite element models using a symmetric eigenstructure assignment technique , 1990 .

[2]  Jaewook Rhim,et al.  A neural network approach for damage detection and identification of structures , 1995 .

[3]  A. Kabe.  Stiffness matrix adjustment using mode data , 1985 .

[4]  Theodore Bartkowicz,et al.  A Two-Step Structural Damage Detection Approach With Limited Instrumentation , 1994 .

[5]  D. Inman,et al.  Matching Finite Element Models to Modal Data , 1990 .

[6]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[7]  Douglas K. Lindner,et al.  Damage detection, location, and estimation for large truss structures , 1993 .

[8]  S. R. Ibrahim,et al.  Correlation of Analysis and Test in Modeling of Structures: Assessment and Review , 1988 .

[9]  Suzanne Weaver Smith,et al.  Underlying modal data issues for detecting damage in truss structures , 1992 .

[10]  Menahern Baruch,et al.  Optimal Weighted Orttiogonalization of Measured Modes , 1978 .

[11]  Layne T. Watson,et al.  Experimental validation of anti-optimization approach for detecting delamination damage , 1995 .

[12]  D. Kammer.  Optimum approximation for residual stiffness in linear system identification , 1988 .

[13]  A. Berman,et al.  Improvement of a Large Analytical Model Using Test Data , 1983 .

[14]  David C. Zimmerman,et al.  An experimental study of structural damage detection using incomplete measurements , 1994 .

[15]  James M. Ricles,et al.  Damage detection in elastic structures using vibratory residual forces and weighted sensitivity , 1992 .

[16]  Paul Sas,et al.  Review of model optimization techniques , 1987 .

[17]  S. Smith,et al.  Optimal identification using inconsistent modal data , 1991 .