Improving Inspection Reliability through Data Fusion of Multi-View Array Data
Lead Research Organisation:
University of Bristol
Department Name: Mechanical Engineering
Abstract
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People |
ORCID iD |
| Paul Wilcox (Principal Investigator) | |
| Yousif Humeida (Researcher) |
Publications
Wilcox PD
(2018)
Quantification of the Effect of Array Element Pitch on Imaging Performance.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Budyn N
(2019)
A model for multi-view ultrasonic array inspection of small two-dimensional defects.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Bevan RLT
(2019)
Experimental Quantification of Noise in Linear Ultrasonic Imaging.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Wilcox PD
(2020)
Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation.
in Proceedings. Mathematical, physical, and engineering sciences
Bevan RLT
(2020)
Data Fusion of Multiview Ultrasonic Imaging for Characterization of Large Defects.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Budyn N
(2021)
Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm
in NDT & E International
| Description | An extremely efficient technique for modelling the sensitivity of multi-view ultrasonic imaging algorithms to small defects of any shape was developed and published. In parallel, a robust experimental protocol for quantifying noise (both random and structural) in multi-view ultrasonic array images of engineering components has been developed and published. Together, these two tools provide the essential cornerstones for fusing multi-view ultrasonic images. Building on this work, a number of methodologies have been developed for fusing multi-view ultrasonic images to increase defect detection and characterisation performance. The most effective methodology for improving detection performance in edge cases (small defects at the limits of detectability in any view) is a modified matched filter approach. This improved edge-case performance requires accurate (sub-millimetre) image co-registration, which is not always achievable in practice. Even if co-registration with this level of accuracy cannot be achieved, data fusion using other techniques has been shown to lead to significant performance improvements in the general case (defects clearly visible in at least one view, but the relevant view depends on defect, location and orientation). This has the potential to significantly reduce operator burden and inspection cost as well as improving reliability. The research has also looked at enhanced characterisation of detected defects using data fusion. For small defects, a Bayesian framework has been developed that compares the measured responses in multiple views to a library of pre-computed responses. From this, a likelihood of each library response matching the measured response is computed to form a map of the most likely defect type and, implicitly, the uncertainty of the characterisation. For larger defects, direct characterisation from ultrasonic images is the most appropriate method but this is extremely challenging for an inspection operator when individual views of a defect only contain partial information. Hence, a further data fusion technique has been developed to combined information from multi-view images into a single image that can be readily interpreted (either by a human operator or automatically). |
| Exploitation Route | We envisage some aspects of work being implemented almost immediately by two of the project partners in their own code, who already have a system capable of capturing the raw ultrasonic data to feed into data fusion algorithms. For wider uptake, we have implemented an initial version of the procedure into our own open-source array data capture and processing software BRAIN to enable others to try them out. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Manufacturing, including Industrial Biotechology,Transport |
| Description | The findings have been disseminated to the industry partners and we continue to collaborate with them via UK Research Centre in NDE. The methods have been implemented in our open-source software, BRAIN, which enables potential users to explore their use for their own applications. Known to have been trialled by Hitachi and BAE Systems and possibly others for specific inspection tasks. The work on masking artefacts undertaken in this project has spurred a wider area of research activity in our group and elsewhere on the general topic of artefact suppression in NDE data. Funding from the Alan Turing Institute for Data Science, industrial money from RCNDE, and a PhD studentship through the FIND CDT has been used to take this work forward as we explore approaches driven by classical statistics as well as machine learning. Overall this supports the continued efforts for automating the interpretation of NDE data in Digital Twins as we move into the Fourth Industrial Revolution. |
| First Year Of Impact | 2020 |
| Sector | Aerospace, Defence and Marine,Energy,Manufacturing, including Industrial Biotechology |
| Impact Types | Economic |
| Description | Non-Destructive Evaluation (NDE) Data Science for Industry 4.0 |
| Amount | £95,848 (GBP) |
| Organisation | Alan Turing Institute |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 02/2022 |
| Title | A model for multi-view ultrasonic array inspection of small two-dimensional defects |
| Description | Supporting data for paper entitled "A model for multi-view ultrasonic array inspection of small two-dimensional defects" in IEEE Trans. Ferroelect., Ultrason. Freq. Contr., 2019. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm |
| Description | Supplementary material to support paper entitled "Characterisation of small embedded two-dimensional defects using multi-view Total Focusing Method imaging algorithm" in press in NDT&E International. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Key part of research undertaken in EP/N015924/1. |
| URL | https://data.bris.ac.uk/data/dataset/2rqavf34p74yi2kj7d267pe02v/ |
| Title | Data from Ultrasonic array data (10-2020) |
| Description | This is the supporting data for paper entitled 'Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation' to be published in Proc. Roy. Soc. A. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://data.bris.ac.uk/data/dataset/gwzymimrki7n28tqodk8n2oqm/ |
| Title | Data fusion of multi-view ultrasonic imaging for characterisation of large defects |
| Description | Supporting data for paper Rhodri L.T. Bevan, Nicolas Budyn, Jie Zhang, Anthony J. Croxford, So Kitazawa and Paul D. Wilcox, "Data fusion of multi-view ultrasonic imaging for characterisation of large defects" in IEEE Trans. Ultrasonics, Ferroelectrics and Frequency Control, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://data.bris.ac.uk/data/dataset/1c46xjhyp8dz221zsfg622y8r9/ |
| Title | Experimental quantification of noise in linear ultrasonic imaging |
| Description | Supporting data for the paper entitled "Experimental quantification of noise in linear ultrasonic imaging" |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Description | BAE Subs |
| Organisation | BAE Systems |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We provide regular updates to all partners through the project review meetings (twice per year), as well as implementing the techniques developed in the project into our open-source array inspection software, Brain. |
| Collaborator Contribution | In addition to direct cash contributions, BAE support the project through attendance at project review meetings and have supplied datasets obtained from welds in small-bore pipework, obtained by them following our procedure. |
| Impact | Papers, procedures and software. |
| Start Year | 2016 |
| Description | EDF |
| Organisation | EDF Energy |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We provide regular updates to all partners through the project review meetings (twice per year), as well as implementing the techniques developed in the project into our open-source array inspection software, Brain. |
| Collaborator Contribution | In addition to direct cash contributions, EDF support the project through attendance at project review meetings, access to samples and datasets. |
| Impact | Various papers, procedures and software. |
| Start Year | 2016 |
| Description | Hitachi |
| Organisation | Hitachi Europe Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We provide regular updates to all partners through the project review meetings (twice per year), as well as implementing the techniques developed in the project into our open-source array inspection software, Brain. |
| Collaborator Contribution | In addition to direct cash contributions, Hitachi support the project through attendance at project review meetings and loaned us two stainless steel samples with stress-corrosion cracking. We have scanned these ultrasonically, Hitachi have X-rayed them and will shortly section them. |
| Impact | Papers, procedures and software. |
| Start Year | 2016 |
| Description | Wood |
| Organisation | Wood Group |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We provide regular updates to all partners through the project review meetings (twice per year), as well as implementing the techniques developed in the project into our open-source array inspection software, Brain. |
| Collaborator Contribution | Wood support the project through attendance at project review meetings and providing access to samples and their ultrasonic scanning systems. |
| Impact | Papers, procedures and software. |
| Start Year | 2016 |
| Title | BRAIN |
| Description | BRAIN is a flexible ultrasonic array data capture and imaging software suite for NDT applications, based around the concept of Full Matrix Capture (FMC) of array data with imaging performed in post-processing. New imaging tools can be readily added to the core. BRAIN is written in Matlab and can be run either from within Matlab or as a standalone Matlab application. |
| Type Of Technology | Software |
| Year Produced | 2010 |
| Open Source License? | Yes |
| Impact | Numerous collaborating companies are known to use BRAIN for trialling new inspection and imaging algorithms. These include Rolls-Royce, BAE System in the UK, Tenaris in Argentina and potentially many others. New array imaging algorithms developed under various programmes are added to BRAIN as they reach maturity. |
| URL | https://sourceforge.net/projects/bristol-brain/ |
| Description | RCNDE Technology Transfer Event |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | RCNDE Technology Transfer events are held annually in January and attract a wide range of primarily industrial participants (including end-users of NDE, NDE supply chain companies, and regulators). The days include a series of short presentations giving the background to the research that is being showcased followed by hands-on practical demonstrations of each technology (typically 4-6 are covered in each event) to small groups of 8-10 people. This provides an opportunity for close interaction with industry. We physically showcased the data fusion techniques being applied to experimental data as it was acquired at the event in Warwick in 2019, and showcased further developments in to 2021 event, which was online due to COVID. |
| Year(s) Of Engagement Activity | 2019,2021 |