Laub, Felix ; Haugwitz, Christoph ; Allevato, Gianni ; Seiler, Julian ; Findeisen, Rolf ; Kupnik, Mario (2023)
Non-contact Lamb wave defect detection based solely on air-coupled ultrasonic phased arrays.
2023 IEEE SENSORS. Vienna, Austria (29.10. - 01.11.2023)
doi: 10.1109/SENSORS56945.2023.10324898
Conference or Workshop Item, Bibliographie
Abstract
Air-coupled ultrasonic non-destructive testing (NDT) enables the inspection of composite materials for defects. In this paper, we investigate non-contact defect detection based on Lamb waves by using two dedicated air-coupled ultrasonic phased arrays, i.e. one for transmission and one for reception. The major challenge is the signal detection of the reflected leaky Lamb wave originating from the defect, despite a strong interfering signal, i.e. the direct air-path wave, causing a so-called blind zone. Therefore, we exploit the angular difference of the reflected leaky Lamb wave and the direct air-path wave by applying high-resolution direction of arrival (DoA) estimation algorithms, i.e. MVDR beamformer (Capon) and MUSIC algorithms, as well as image deconvolution methods, i.e. CLEAN and Richardson-Lucy algorithms. First, we assess the signal detection capability by using Monte Carlo simulations considering realistic non-ideal array elements. Second, we validate the simulations by conducting testbed measurements, which show that conventional beamforming (CBF), MVDR and MUSIC are capable of fully air-coupled defect detection, even when the direct air-path wave is impinging. We conclude that the blind zone can be eliminated and show that the MUSIC algorithm excels in highlighting the reflected leaky Lamb wave.
Item Type: | Conference or Workshop Item |
---|---|
Erschienen: | 2023 |
Creators: | Laub, Felix ; Haugwitz, Christoph ; Allevato, Gianni ; Seiler, Julian ; Findeisen, Rolf ; Kupnik, Mario |
Type of entry: | Bibliographie |
Title: | Non-contact Lamb wave defect detection based solely on air-coupled ultrasonic phased arrays |
Language: | English |
Date: | 28 November 2023 |
Place of Publication: | Piscataway |
Publisher: | IEEE |
Book Title: | IEEE SENSORS 2023: Conference Proceedings |
Event Title: | 2023 IEEE SENSORS |
Event Location: | Vienna, Austria |
Event Dates: | 29.10. - 01.11.2023 |
DOI: | 10.1109/SENSORS56945.2023.10324898 |
Abstract: | Air-coupled ultrasonic non-destructive testing (NDT) enables the inspection of composite materials for defects. In this paper, we investigate non-contact defect detection based on Lamb waves by using two dedicated air-coupled ultrasonic phased arrays, i.e. one for transmission and one for reception. The major challenge is the signal detection of the reflected leaky Lamb wave originating from the defect, despite a strong interfering signal, i.e. the direct air-path wave, causing a so-called blind zone. Therefore, we exploit the angular difference of the reflected leaky Lamb wave and the direct air-path wave by applying high-resolution direction of arrival (DoA) estimation algorithms, i.e. MVDR beamformer (Capon) and MUSIC algorithms, as well as image deconvolution methods, i.e. CLEAN and Richardson-Lucy algorithms. First, we assess the signal detection capability by using Monte Carlo simulations considering realistic non-ideal array elements. Second, we validate the simulations by conducting testbed measurements, which show that conventional beamforming (CBF), MVDR and MUSIC are capable of fully air-coupled defect detection, even when the direct air-path wave is impinging. We conclude that the blind zone can be eliminated and show that the MUSIC algorithm excels in highlighting the reflected leaky Lamb wave. |
Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
Date Deposited: | 13 Mar 2024 10:42 |
Last Modified: | 21 Jun 2024 12:21 |
PPN: | 519326083 |
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