Abstract
This paper proposes a deep learning framework for artefact identification and suppression in the context of non-destructive evaluation. The model, based on the concept of autoencoders, is developed for enhancing ultrasound inspection and defect identification through images obtained from full matrix capture data and the total focusing method. An experimental case study is used to prove the effectiveness of the method while exploring its practical limitations. A comparison with a state-of-the-art methodology based on image analysis is addressed for the identification and suppression of artefacts. In general, the proposed method efficiently provides accurate suppression of artefacts in complex scenarios, even when the defect is located below the footprint of the ultrasonic probe, and also yields the physical parameters needed for imaging as a by-product.
Original language | English |
---|---|
Article number | 102575 |
Journal | NDT and E International |
Volume | 126 |
Early online date | 16 Nov 2021 |
DOIs | |
Publication status | Published - 1 Mar 2022 |
Bibliographical note
Funding Information:The work reported is part of a pilot project (Grant number 100374) funded by Lloyd's Register Foundation and the Alan Turing Institute Data-Centric Engineering Programme. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol ? http://www.bris.ac.uk/acrc/.
Funding Information:
The work reported is part of a pilot project (Grant number 100374 ) funded by Lloyd's Register Foundation and the Alan Turing Institute Data-Centric Engineering Programme. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol – http://www.bris.ac.uk/acrc/ .
Publisher Copyright:
© 2021 Elsevier Ltd
Keywords
- Non-destructive evaluation
- Deep learning
- Autoencoders
- Ultrasound
- Phased-arrays
- Artefact identification
- Full matrix capture
- Total focusing method
Fingerprint
Dive into the research topics of 'A deep learning based methodology for artefact identification and suppression with application to ultrasonic images'. Together they form a unique fingerprint.Projects
-
Data Science for NDE
Wilcox, P. D. (Principal Investigator), Croxford, A. J. (Co-Investigator) & Cantero Chinchilla, S. (Researcher)
Project: Research
Equipment
-
HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities
Alam, S. R. (Manager), Williams, D. A. G. (Manager), Eccleston, P. E. (Manager) & Greene, D. (Manager)
Facility/equipment: Facility