Paper
26 July 2004 Structural damage identification using embedded sensitivity functions
Timothy J. Johnson, Chulho Yang, Douglas E. Adams, Sam Ciray
Author Affiliations +
Abstract
Vibration-based damage identification using embedded sensitivity functions is discussed. These sensitivity functions are computed directly from experimental frequency response functions and reflect changes in the forced response of structural systems when mass, damping or stiffness parameters are changed. The theory of embedded sensitivity functions is reviewed and applied to characterize damage in a simulated three degree-of-freedom system and a full-scale exhaust system with nonlinear characteristics. Linear damage is shown to be properly detected, located and quantified in theory and practice for structures with one damage mechanism by comparing embedded sensitivity functions with finite difference frequency response functions in undamaged and damaged test data. It is also shown using the exhaust system that false indications of damage due to nonlinear amplitude dependence can be avoided by developing nonlinear baseline models. Experimental results indicate that the technique is most effective when changes to frequency response functions are no larger than 10% to avoid distortions in the estimated perturbations due to variations in the sensitivity functions.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Timothy J. Johnson, Chulho Yang, Douglas E. Adams, and Sam Ciray "Structural damage identification using embedded sensitivity functions", Proc. SPIE 5390, Smart Structures and Materials 2004: Smart Structures and Integrated Systems, (26 July 2004); https://doi.org/10.1117/12.541741
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Complex systems

Data modeling

Structural health monitoring

Diagnostics

Systems modeling

Computing systems

Corrosion

Back to Top