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Machine learning-based corrosion-like defect estimation with shear-horizontal guided waves improved by mode separation
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de Castro Ribeiro, Mateus Gheorghe, Kubrusly, Alan Conci, Ayala, Helon Vicente Hultmann and Dixon, Steve M. (2021) Machine learning-based corrosion-like defect estimation with shear-horizontal guided waves improved by mode separation. IEEE Access, 9 . pp. 40836-40849. doi:10.1109/ACCESS.2021.3063736 ISSN 2169-3536.
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WRAP-machine-learning-based-corrosion-like-defect-estimation-shear-horizontal-guided-waves-improved-mode-separation-Dixon-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (3254Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2021.3063736
Abstract
Shear Horizontal (SH) guided waves have been extensively used to estimate and detect defects in structures like plates and pipes. Depending on the frequency and plate thickness, more than one guided-wave mode propagates, which renders signal interpretation complicated due to mode mixing and complex behavior of each individual mode interacting with defects. This paper investigates the use of machine learning models to analyse the two lowest order SH guided modes, for quantitative size estimation and detection of corrosion-like defects in aluminium plates. The main contribution of the present work is to show that mode separation through machine learning improves the effectiveness of predictive models. Numerical simulations have been performed to generate time series for creating the estimators, while experimental data have been used to validate them. We show that a full mode separation scheme decreased the error rate of the final model by 30% and 67% in defect size estimation and detection respectively.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Shear waves , Plates, Aluminum, Plates, Aluminum -- Corrosion -- Simulation methods, Neural networks (Computer science) , Structural engineering -- Computer programs, Structural health monitoring | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 4 March 2021 | ||||||
| Dates: |
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| Volume: | 9 | ||||||
| Page Range: | pp. 40836-40849 | ||||||
| DOI: | 10.1109/ACCESS.2021.3063736 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 26 May 2021 | ||||||
| Date of first compliant Open Access: | 26 May 2021 | ||||||
| Is Part Of: | 1 |
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