Ultrasonic methods have been implemented for <i>in situ</i> sizing of fatigue cracks near fastener holes. These techniques,
however, only provide an estimate at the time of the measurement and cannot predict the remaining
life of the structure. In contrast, statistical crack propagation approaches model the expected fatigue life based
on worst-case fatigue process assumptions. The authors have recently developed a Kalman filter approach for
combining ultrasonic observations with crack growth laws. An ultrasonic angle-beam technique, combined with
an energy-based wave propagation model, serves as the measurement model. Paris's crack growth equation acts
as the system model for crack propagation. For simulated data, this approach provided more accurate crack
size estimates than either the ultrasonic measurements or crack growth approach alone. Presented here are
experimental results to assess the ability of the Kalman filter to provide reasonable crack size estimates.
Diffuse ultrasonic signals received from ultrasonic sensors which are permanently mounted near, on or in critical structures of complex geometry are very difficult to interpret because of multiple modes and reflections constructively and destructively interfering. Both changing environmental and structural conditions affect the ultrasonic wave field, and the resulting changes in the received signals are similar and of the same magnitude. This paper describes a differential feature-based classifier approach to address the problem of determining if a structural change has actually occurred. Classifiers utilizing time and frequency domain features are compared to classifiers based upon time-frequency representations. Experimental data are shown from a metallic specimen subjected to both environmental changes and the introduction of artificial damage. Results show that both types of classifiers are successful in discriminating between environmental and structural changes. Furthermore, classifiers developed for one particular structure were successfully applied to a second one that was created by modifying the first structure. Best results were obtained using a classifier based upon features calculated from time-frequency regions of the spectrogram.