Nondestructive evaluation (NDE) is widely used in the aerospace industry, using scheduled maintenance inspections to detect cracks or other anomalies in structural and rotating components. Life prediction and inspection interval decisions in aerospace applications require knowledge of the size distribution of unknown existing cracks and the probability of detecting (POD) a crack, as a function of crack characteristics (e.g., crack length). The POD for a particular inspection method is usually estimated through laboratory experiments on a given specimen set. These experiments, however, cannot duplicate the conditions of in-service inspections. Quantifying the size distribution of unknown existing cracks is more difficult. If NDE signal strength is recorded at all inspections and if crack-length information is obtained after ‘‘crack find’’ inspections, it is possible to estimate the joint distribution of crack length, noise response, and signal response. This joint distribution can then be used to estimate both the in-service POD and the crack-length distribution at a given period of service time. In this article, we present a statistical model to describe the data and illustrate a Bayesian method to do the estimation and quantify uncertainty.
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