Substructure Identification and Health Monitoring Using Noisy Response Measurements Only

TLDR: A substructuring approach that allows for the identification and monitoring of some critical substructures only and allows one to obtain not only the most probable values of the updated model parameters but also their as- sociated uncertainties using only one set of response data is proposed.

Abstract: A probabilistic substructure identification and health monitoring methodology for linear systems is presented using measured response time histories only. A very large number of uncertain parameters have to be identified if one considers the updating of the entire structure. For identifiability, one then would require a very large number of sensors. Furthermore, even when such a large number of sensors are available, process- ing of vast amount of the corresponding data raises com- putational difficulties. In this article a substructuring ap- proach is proposed, which allows for the identification and monitoring of some critical substructures only. The proposed method does not require any interface measure- ments and/or excitation measurements. No information regarding the stochastic model of the input is required. Specifically, the method does not require the response to be stationary and does not assume any knowledge of the parametric form of the spectral density of the input. There- fore, the method has very wide applicability. The proposed approach allows one to obtain not only the most probable values of the updated model parameters but also their as- sociated uncertainties using only one set of response data. The probability of damage can be computed directly using data from the undamaged and possibly damaged struc- ture. A hundred-story building model is used to illustrate the proposed method.