On a Model-based Calibration Approach to Dynamic Baseline Estimation for Structural Health Monitoring

Sparse arrays of permanently attached ultrasonic transducers are capable of quickly interrogating large areas, thereby reducing or eliminating the need for extensive bulk wave testing in plate-like structures. Current imaging methods often ignore dispersion, analyzing received signals by approximating wave propagation with a nominal group velocity. Dispersion compensation can significantly improve the resolution and accuracy of such methods by more accurately modeling the propagation environment and thereby leveraging a larger percentage of the energy and information contained in broadband signals. By adaptively estimating system parameters such as dispersion and relative transducer locations, algorithms will operate at the time of test with the most accurate model possible and will thus be robust to homogeneous environmental changes. This paper reviews features and limitations of current dispersion estimation algorithms and describes a new model-based approach that avoids a priori information and relies only on the physical nature of the dispersion curves. Performance of two contributing algorithms are evaluated using simulated signals obtained from known dispersion curves.