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Details of Grant 

EPSRC Reference: EP/N015533/1
Title: Improving Inspection Reliability through Data Fusion of Multi-View Array Data
Principal Investigator: Cawley, Professor P
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Amec Foster Wheeler UK BAE Systems EDF Energy
Hitachi
Department: Dept of Mechanical Engineering
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 April 2016 Ends: 31 March 2019 Value (£): 287,770
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Materials testing & eng.
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Energy
Related Grants:
EP/N015924/1
Panel History:
Panel DatePanel NameOutcome
25 Nov 2015 Engineering Prioritisation Panel Meeting 25th and 26th November 2015 Announced
Summary on Grant Application Form
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.
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Organisation Website: http://www.imperial.ac.uk