Pedram, Kamran ORCID: 0000-0002-9770-0729, Gan, Tat-Hean and Ghafourian, Mahdieh (2020) Improved defect detection of guided wave testing using split-spectrum processing. Sensors, 20 (17):4759. ISSN 1424-8220 (Online) (doi:https://doi.org/10.3390/s20174759)

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Abstract

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.

Item Type:	Article
Additional Information:	This article belongs to the Special Issue Advances in Ultrasonic Guided Wave Sensor Technologies for Structural Health Monitoring.
Uncontrolled Keywords:	guided wave testing; signal processing; SSP; SNR
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	12 May 2023 14:03
URI:	http://gala.gre.ac.uk/id/eprint/29973

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