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Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave

Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave

Pedram, Seyed Kamran, Fateri, Sina, Gan, Lu, Haig, Alex and Thornicroft, Keith (2017) Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave. Ultrasonics, 83. pp. 48-59. ISSN 0041-624X (doi:https://doi.org/10.1016/j.ultras.2017.08.002)

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Abstract

Ultrasonic guided wave (UGW) systems are broadly used in several branches of industry where the structural integrity is of concern. In those systems, signal interpretation can often be challenging due to the multi-modal and dispersive propagation of UGWs. This results in degradation of the signals in terms of signal-to-noise ratio (SNR) and spatial resolution. This paper employs the split-spectrum processing (SSP) technique in order to enhance the SNR and spatial resolution of UGW signals using the optimized filter bank parameters in real time scenario for pipe inspection. SSP technique has already been developed for other applications such as conventional ultrasonic testing for SNR enhancement. In this work, an investigation is provided to clarify the sensitivity of SSP performance to the filter bank parameter values for UGWs such as processing bandwidth, filter bandwidth, filter separation and a number of filters. As a result, the optimum values are estimated to significantly improve the SNR and spatial resolution of UGWs. The proposed method is synthetically and experimentally compared with conventional approaches employing different SSP recombination algorithms. The Polarity Thresholding (PT) and PT with Minimization (PTM) algorithms were found to be the best recombination algorithms. They substantially improved the SNR up to 36.9 dB and 38.9 dB respectively. The outcome of the work presented in this paper paves the way to enhance the reliability of UGW inspections.

Item Type: Article
Uncontrolled Keywords: signal processing, SNR, split-spectrum processing, ultrasonic guided wave testing
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 17 Oct 2019 11:10
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/25284

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