Data fusion for improving the reliability of automated non-destructive inspection

In automated NDE a region of an inspected component is typically interrogated several times, be it within a single data channel, across multiple channels or over the course of repeated inspections. The systematic combination of these diverse readings is recognised to provide a means to improve the reliability of the inspection, for example by enabling noise suppression. Specifically, such data fusion makes it possible to declare regions of the component defect-free to a very high probability whilst readily identifying indications. The paper consists of two parts: the first addresses the computational challenges associated with indication detection in large datasets, while the latter outlines an approach to combining different sections of different amplitude fields for detecting likely indications and evaluating associated probabilities, involving spatial statistics.