Damage detection with ambient vibration data using time series modeling

Abstract

In this study, a novel approach using a modified time series analysis methodology is used to detect, locate, and quantify structural changes by using ambient vibration data. Random Decrement (RD) is used to obtain pseudo free response data from the ambient vibration time histories. ARX models (Auto-Regressive models with exogenous input) are created for different sensor clusters by using the pseudo free response of the structure. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. After the ARX models for the healthy structure for each sensor cluster are created, the same models are used for predicting the data from the damaged structure. The difference between the fit ratios is used as damage indicating feature. The methodology is applied to experimental data obtained from steel grid structure tests and it is shown that the approach is successfully used for identification, localization, and quantification of different damage cases for both impact and ambient tests. ©2010 Society for Experimental Mechanics Inc.