System Identification Approach to Detection of Structural Changes

Abstract

This paper explores the potential of a time‐domain identification procedure to detect structural changes on the basis of noise‐polluted measurements. The method of approach requires the use of excitation and acceleration response records, to develop an equivalent multi‐degree‐of‐freedom (MDOF) mathematical model whose order is compatible with the number of sensors used. Application of the identification procedure under discussion yields the optimum value of the elements of equivalent linear system matrices. By performing the identification task before and after potential structural changes (damage) in the physical system have occurred, quantifiable changes in the identified mathematical model can be detected. The usefulness of the identification procedure under discussion for damage detection is demonstrated by means of an example three‐degree‐of‐freedom (DOF) linear system. This system is used to conduct synthetic experiments to generate noise‐polluted “data” sets that are subsequently analyzed to determine the mean, variance, and probability density function corresponding to each element of the identified system matrices. Different versions of the model are investigated in which the location as well as the magnitude of the “damage” is varied. On the basis of this exploratory study, it appears that determining the probability density functions of the identified system matrices may furnish useful indices that can be conveniently extracted during an experimental test, to quantify changes in the characteristics of physical systems.