Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23517
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dc.contributor.authorPedram, SK-
dc.contributor.authorGan, TH-
dc.contributor.authorGhafourian, M-
dc.date.accessioned2021-11-15T09:37:40Z-
dc.date.available2020-09-01-
dc.date.available2021-11-15T09:37:40Z-
dc.date.issued2020-08-23-
dc.identifier4759-
dc.identifier.citationPedram, S.K., Gan, T.-H. and Ghafourian, M. (2020) ‘Improved Defect Detection of Guided Wave Testing Using Split-Spectrum Processing’, Sensors, 20 (17), 4759, pp. 1-18. doi: 10.3390/s20174759.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/23517-
dc.description.abstract© 2020 by the authors. Ultrasonic guided wave (UGW) testing is widely applied in numerous industry areas for the examination of pipelines where structural integrity is of concern. Guided wave testing is capable of inspecting long lengths of pipes from a single tool location using some arrays of transducers positioned around the pipe. Due to dispersive propagation and the multimodal behavior of UGW, the received signal is usually degraded and noisy, that reduce the inspection range and sensitivity to small defects. Therefore, signal interpretation and identifying small defects is a challenging task in such systems, particularly for buried/coated pipes, in that the attenuation rates are considerably higher compared with a bare pipe. In this work, a novel solution is proposed to address this issue by employing an advanced signal processing approach called “split-spectrum processing” (SSP) to minimize the level of background noise and enhance the signal quality. The SSP technique has already shown promising results in a limited trial for a bar pipe and, in this work, the proposed technique has been experimentally compared with the traditional approach for coated pipes. The results illustrate that the proposed technique significantly increases the signal-to-noise ratio and enhances the sensitivity to small defects that are hidden below the background noise.en_US
dc.description.sponsorshipGreenwich University (Internal funds code 13265-0641-R08584); Innovation UK fund managed by The Welding Institute (TWI) Ltd. (reference 102077, project IC0513 (TWI project 30034) in partnership of Brunel University.en_US
dc.format.extent1 - 18 (18)-
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectguided wave testingen_US
dc.subjectsignal processingen_US
dc.subjectSSPen_US
dc.subjectSNRen_US
dc.titleImproved defect detection of guided wave testing using split-spectrum processingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/s20174759-
dc.relation.isPartOfSensors (Switzerland)-
pubs.issue17-
pubs.publication-statusPublished-
pubs.volume20-
dc.identifier.eissn1424-8220-
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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