A new extension of unscented Kalman filter for structural health assessment with unknown input

A time-domain nonlinear system identification (SI)-based structural health assessment (SHA) procedure, using Unscented Kalman Filter (UKF) concept, is presented in this paper. It is a two-stage procedure. It integrates an iterative least squares technique and the unscented Kalman filter concept. The authors believe that the integrated procedure significantly improves the basic UKF concept. The procedure can assess the health of a structure using only a limited number of noise-contaminated acceleration time-histories measured only at a small part of a structure and does not need information on input excitation. The structures are represented by finite element models and the location and severity of defect(s) are assessed by tracking the changes in the stiffness properties of individual elements from their expected values. With the help of examples, it is demonstrated that the method is capable of accurately identifying defect-free and defective states of structures. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. It is demonstrated that the accuracy of the method is much better than the other methods currently available for the structural health assessment. It is also superior to the extended Kalman filter. Considering the accuracy and robustness, the procedure can be used as a nondestructive structural health assessment procedure.