Ultrasonic interrogation of metal alloys has been demonstrated to be effective for monitoring fatigue-induced damage in a structural health monitoring (SHM) framework. Before such a method can be implemented, the probability of detection (POD) as a function of crack size must be quantified. POD curves are routinely generated for nondestructive evaluation (NDE) methods, typically by performing large numbers of measurements to capture the variability arising from variations in operators, probes, instruments and crack morphology. Such studies have not yet been carried out for many, if any, SHM methods, and thus identifying and quantifying relevant sources of variability have not generally been addressed. Considered here is monitoring of fastener holes for fatigue cracks, and POD curves are generated using essentially the same methods as are used for NDE but with differences in setting detection thresholds. Interpretation of the curves is discussed given that operator, sensor and instrumentation variability are no longer issues in the context of monitoring a specific structure.
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 timefrequency 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.
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