Improving Inspection Reliability through Data Fusion of Multi-View Array Data

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The objective of this project is to obtain a step-change improvement in the detection and characterisation of defects in safety-critical components across a range of industries including nuclear power generation and the defence sector. This will be achieved through data-fusion of the multiple views of a component's interior that can be obtained through modern ultrasonic array imaging techniques. Previous work by the team has demonstrated a two-order-of-magnitude improvement in detection performance when data fusion was applied to ultrasonic data obtained from separate scans performed with single-element probes. This was in a case where the expected defects were small, point-like inclusions that scatter roughly uniformly in all directions. The proposed project will develop the data-fusion philosophy for improving defect detection performance from multi-view array data in the much more complex case where the defect morphology cannot be assumed in advance and the scattering pattern may be strongly directional. Therefore, the project will necessarily address the critical challenges of applying data fusion to defect classification and sizing from multi-view array data. Demonstrator software will be produced that will show an image of the test component with indications ranked by the probability of them being produced by a defect; it will then be possible to probe any of these indications to show detailed classification (e.g. crack, void, inclusion etc.) and sizing information. The project is supported by EDF, Hitachi, BAE Systems and AMEC Foster Wheeler.