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http://hdl.handle.net/10397/105308
Title: | Auto-diagnosis of time-of-flight for ultrasonic signal based on defect peaks tracking model | Authors: | Yang, F Shi, D Lo, LY Mao, Q Zhang, J Lam, KH |
Issue Date: | Feb-2023 | Source: | Remote sensing, Feb. 2023, v. 15, no. 3, 599 | Abstract: | With the popularization of humans working in tandem with robots and artificial intelligence (AI) by Industry 5.0, ultrasonic non-destructive testing (NDT)) technology has been increasingly used in quality inspections in the industry. As a crucial part of handling ultrasonic testing results–signal processing, the current approach focuses on professional training to perform signal discrimination but automatic and intelligent signal optimization and estimation lack systematic research. Though the automated and intelligent framework for ultrasonic echo signal processing has already exhibited essential research significance for diagnosing defect locations, the real-time applicability of the algorithm for the time-of-flight (ToF) estimation is rarely considered, which is a very important indicator for intelligent detection. This paper conducts a systematic comparison among different ToF algorithms for the first time and presents the auto-diagnosis of the ToF approach based on the Defect Peaks Tracking Model (DPTM). The proposed DPTM is used for ultrasonic echo signal processing and recognition for the first time. The DPTM using the Hilbert transform was verified to locate the defect with the size of 2–10 mm, in which the wavelet denoising method was adopted. With the designed mechanical fixture through 3D printing technology on the pipeline to inspect defects, the difficulty of collecting sufficient data could be conquered. The maximum auto-diagnosis error could be reduced to 0.25% and 1.25% for steel plate and pipeline under constant pressure, respectively, which were much smaller than those with the DPTM adopting the cross-correlation. The real-time auto-diagnosis identification feature of DPTM has the potential to be combined with AI in future work, such as machine learning and deep learning, to achieve more intelligent approaches for industrial health inspection. | Keywords: | Intelligent algorithm NDT Pipeline inspection Smart manufacturing Time-of-flight Ultrasound transducer |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Remote sensing | EISSN: | 2072-4292 | DOI: | 10.3390/rs15030599 | Rights: | © 2023 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/). The following publication ang F, Shi D, Lo L-Y, Mao Q, Zhang J, Lam K-H. Auto-Diagnosis of Time-of-Flight for Ultrasonic Signal Based on Defect Peaks Tracking Model. Remote Sensing. 2023; 15(3):599 is available at https://doi.org/10.3390/rs15030599. |
Appears in Collections: | Journal/Magazine Article |
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remotesensing-15-00599.pdf | 6.49 MB | Adobe PDF | View/Open |
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