Quantitative non-destructive imaging with limited data

Lead Research Organisation: Imperial College London
Department Name: Dept of Mechanical Engineering


If imaging required less data, it would enable faster throughput, improved performance in restricted access situations and simpler, cheaper hardware. The information from images enables damage to be accurately quantified within engineering components, avoiding the need to choose between excessive conservatism and unpredicted failures. To enable improved reconstructions from limited data sets, a diverse set of approaches have been identified, incorporating knowledge of physical wave interaction with objects, use of external information, image processing and other techniques. The fellowship will address the broad problem by applying these approaches to several example applications which are of great interest to industry, and will ultimately enable the development of the field of limited data imaging. While primarily focused on NDE (non-destructive evaluation), the applications of this spread to areas including medicine, geophysics and security.

Planned Impact

The high profile incidents such as the A380 engine explosion in Singapore in 2010, or the Deepwater Horizon accident, can cause significant damage to the environment and place people's health, or worse lives, at risk. Even small, unpredicted failures can have a significant financial cost associated with them. A solution of being more conservative, e.g. replacing components well before the end of their predicted lives, might reduce this risk, but necessitates the wasting of resources at great cost.

NDE provides crucial information to enable components to be used to their maximum without the risk of failure, and the improvements envisaged in this fellowship, looking at improved characterisation from limited data, promise to deliver this information at reduced cost, in less time, and with reduced access requirements. The ultimate outcome of the research clearly promises to have significant impact on society and the economy and I will ensure that the academic work is managed through to application to maximise this impact.

This research is undertaken to be closely associated with industrial needs. I have identified three companies (Rolls-Royce Aero and Submarine divisions, Tenaris and BP) with whom I have already collaborated to identify important applications of my research, and I will have regular 6-monthly steering meetings with them to ensure that all future work is well aligned to their needs. These companies are all members of the RCNDE, a body led by Imperial College, consisting of six UK universities and 16 industrial members: Airbus, AMEC, BAE Systems, BP, EDF Energy, National Nuclear Laboratory, Defence Science and Technology Laboratory (DSTL), E.ON Engineering Ltd, GKN, Office for Nuclear Regulation (ONR), Hitachi, Petrobras, Rolls-Royce Plc., Shell, SKF and Tenaris, covering the nuclear, oil & gas, power, defence, aerospace and transportation industries. I will look to broaden the impact of my work outside the three identified companies by inviting additional RCNDE industrial members to review meetings to identify additional applications of my research.

Beyond my collaborations and planned meetings with the three identified companies, I will share any breakthroughs with the broader industrial community through a number of avenues. I will take advantage of the general engineering audience at the annual research showcase within the Mechanical Engineering Department to present my work both via formal plenary presentations and through less formal discussions with smaller groups visiting the NDE lab. My involvement in RCNDE meetings also provides an opportunity to share my work with a more focused industrial NDE community through presentations during the regular RCNDE industrial visits to Imperial. Any promising outcomes of these discussions can be followed up with invitations to the more focused review meetings discussed above. The RCNDE also hosts regular technology transfer events and I plan to exploit these to explain the processes and techniques behind my research outcomes to industry.

The Imperial College NDE group has an extremely strong track record for the delivery of technology to industry, with two successful spin-out companies and several license deals. All commercialisation of Imperial College intellectual property must be undertaken via Imperial Innovations Ltd., which provides an important pathway to enable academic advances to be licensed and sold to industry. I will exploit this to deliver the more promising outcomes of my research to industrial application. There are 30 associate members in the RCNDE involved in the supply chain (many of them SMEs), and I will arrange to engage with them to help deliver solutions to industry. I plan to develop connections with manufacturers of acquisition equipment for both ultrasound and radiography through platforms such as the BINDT conference, to discuss implementation options.


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Huthwaite P (2016) Guided wave tomography with an improved scattering model. in Proceedings. Mathematical, physical, and engineering sciences
Huthwaite P (2016) Eliminating incident subtraction in diffraction tomography. in Proceedings. Mathematical, physical, and engineering sciences
Huthwaite P (2016) Improving accuracy through density correction in guided wave tomography. in Proceedings. Mathematical, physical, and engineering sciences
Seher M (2016) Experimental Studies of the Inspection of Areas With Restricted Access Using A0 Lamb Wave Tomography. in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Title Guided wave tomography test data 
Description A data set for the research community to test guided wave tomography algorithms and enable reliable comparisons to be drawn between different groups. 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes  
Impact N/A at present. 
URL http://dx.doi.org/10.5281/zenodo.44626