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Enhancement of ultrasonic guided wave signals using a split-spectrum processing method

Enhancement of ultrasonic guided wave signals using a split-spectrum processing method

Pedram, Seyed Kamran ORCID: 0000-0002-9770-0729, Mudge, Peter and Gan, Tat-Hean (2018) Enhancement of ultrasonic guided wave signals using a split-spectrum processing method. Applied sciences, 8 (10):1815. ISSN 2076-3417 (doi:https://doi.org/10.3390/app8101815)

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Abstract

Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection.

Item Type: Article
Uncontrolled Keywords: signal processing, snr, split-spectrum processing, ultrasonic guided waves
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 21 Oct 2020 13:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 2
URI: http://gala.gre.ac.uk/id/eprint/25484

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