The Computational Enhancement of Automated Non-Destructive Inspection

Abstract

In industrial NDE it is increasingly common for data acquisition to be automated, driving a recent substantial increase in the availability of data. The collected data need to be analysed and currently this is largely done manually by a skilled operator - a rather painstaking task given how rarely defects occur. Moreover, in automated NDE a region of an inspected component is typically interrogated several times, be it within a single data channel due to multiple probe passes, across several channels acquired simultaneously or over the course of repeated inspections. The systematic combination of these diverse readings is recognised to offer an opportunity to improve the reliability of the inspection, for example by enabling noise suppression, but is not achievable in a manual analysis. Hence there is scope for the inspection reliability to be improved whilst reducing the time taken for the data analysis by computational means. This thesis describes the development of a software framework providing a partial automation capability, aligning then fusing the available experimental data to declare regions of the component defect-free to a very high probability whilst readily identifying indications, thereby optimising the use of the operator's time. The framework is designed to be applicable to a wide range of automated NDE scenarios, but the focus in development has been on two distinct, industrial inspections: the ultrasonic inspection of power station turbine rotor bores and the ultrasonic immersion inspection of aerospace turbine disks. Results obtained for industrial datasets from these two applications convincingly demonstrate the benefits of using the developed software system.