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Sanchez Duo I, Lanzagorta JL, Aizpurua Maestre I, Galdos L. Enhancing Time-of-Flight Diffraction (TOFD) Inspection through an Innovative Curved-Sole Probe Design. SENSORS (BASEL, SWITZERLAND) 2024; 24:6360. [PMID: 39409400 PMCID: PMC11479281 DOI: 10.3390/s24196360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 09/04/2024] [Accepted: 09/27/2024] [Indexed: 10/20/2024]
Abstract
Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach.
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Affiliation(s)
- Irati Sanchez Duo
- IDEKO Member of Basque Research and Technology Alliance, 20870 Elgoibar, Gipuzkoa, Spain; (J.L.L.); (I.A.M.)
| | - Jose Luis Lanzagorta
- IDEKO Member of Basque Research and Technology Alliance, 20870 Elgoibar, Gipuzkoa, Spain; (J.L.L.); (I.A.M.)
| | - Iratxe Aizpurua Maestre
- IDEKO Member of Basque Research and Technology Alliance, 20870 Elgoibar, Gipuzkoa, Spain; (J.L.L.); (I.A.M.)
| | - Lander Galdos
- Faculty of Engineering, Mondragon Unibertsitatea, 20500 Mondragon, Gipuzkoa, Spain;
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Wang W, Chen J, Han G, Shi X, Qian G. Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:5944. [PMID: 39338689 PMCID: PMC11435956 DOI: 10.3390/s24185944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Revised: 09/12/2024] [Accepted: 09/12/2024] [Indexed: 09/30/2024]
Abstract
Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.
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Affiliation(s)
- Weihua Wang
- State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
| | - Jiugong Chen
- State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
| | - Gangsheng Han
- State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
| | - Xiushan Shi
- State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
| | - Gong Qian
- State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
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Huang J, Chen P, Li R, Fu K, Wang Y, Duan J, Li Z. Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2699. [PMID: 38732805 PMCID: PMC11085684 DOI: 10.3390/s24092699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.
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Affiliation(s)
- Jie Huang
- College of Mechanical and Storage and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China;
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Pengchao Chen
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Rui Li
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Kuan Fu
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Yanan Wang
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Jinyao Duan
- General Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China; (P.C.); (R.L.); (K.F.); (Y.W.); (J.D.)
| | - Zhenlin Li
- College of Mechanical and Storage and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China;
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Chen M, Xu X, Yang K, Wu H. Full-Matrix Imaging in Fourier Domain towards Ultrasonic Inspection with Wide-Angle Oblique Incidence for Welded Structures. SENSORS (BASEL, SWITZERLAND) 2024; 24:832. [PMID: 38339549 PMCID: PMC10857186 DOI: 10.3390/s24030832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/13/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
The total focusing method (TFM) has been increasingly applied to weld inspection given its high image quality and defect sensitivity. Oblique incidence is widely used to steer the beam effectively, considering the defect orientation and structural complexity of welded structures. However, the conventional TFM based on the delay-and-sum (DAS) principle is time-consuming, especially for oblique incidence. In this paper, a fast full-matrix imaging algorithm in the Fourier domain is proposed to accelerate the TFM under the condition of oblique incidence. The algorithm adopts the Chebyshev polynomials of the second kind to directly expand the Fourier extrapolator with lateral sound velocity variation. The direct expansion maintains image accuracy and resolution in wide-angle situations, covering both small and large angles, making it highly suitable for weld inspection. Simulations prove that the third-order Chebyshev expansion is required to achieve image accuracy equivalent to the TFM with wide-angle incidence. Experiments verify the algorithm's performance for weld flaws using the proposed method with the transverse wave and the full-skip mode. The depth deviation is within 0.53 mm, and the sizing error is below 15%. The imaging efficiency is improved by a factor of up to 8 compared to conventional TFM. We conclude that the proposed method is applicable to high-speed weld inspection with various oblique incidence angles.
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Affiliation(s)
- Mu Chen
- The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (M.C.); (X.X.); (K.Y.)
| | - Xintao Xu
- The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (M.C.); (X.X.); (K.Y.)
| | - Keji Yang
- The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (M.C.); (X.X.); (K.Y.)
| | - Haiteng Wu
- Hangzhou Shenhao Technology Co., Ltd., Hangzhou 311121, China
- Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot, Hangzhou 311121, China
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Uhlig S, Alkhasli I, Schubert F, Tschöpe C, Wolff M. A review of synthetic and augmented training data for machine learning in ultrasonic non-destructive evaluation. ULTRASONICS 2023; 134:107041. [PMID: 37352575 DOI: 10.1016/j.ultras.2023.107041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 05/05/2023] [Accepted: 05/06/2023] [Indexed: 06/25/2023]
Abstract
Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent years, promoting higher-level automation and decision-making in flaw detection and classification. Building a generalized training dataset to apply ML in non-destructive evaluation (NDE), and thus UT, is exceptionally difficult since data on pristine and representative flawed specimens are needed. Yet, in most UT test cases flawed specimen data is inherently rare making data coverage the leading problem when applying ML. Common data augmentation (DA) strategies offer limited solutions as they don't increase the dataset variance, which can lead to overfitting of the training data. The virtual defect method and the recent application of generative adversarial neural networks (GANs) in UT are sophisticated DA methods targeting to solve this problem. On the other hand, well-established research in modeling ultrasonic wave propagations allows for the generation of synthetic UT training data. In this context, we present a first thematic review to summarize the progress of the last decades on synthetic and augmented UT training data in NDE. Additionally, an overview of methods for synthetic UT data generation and augmentation is presented. Among numerical methods such as finite element, finite difference, and elastodynamic finite integration methods, semi-analytical methods such as general point source synthesis, superposition of Gaussian beams, and the pencil method as well as other UT modeling software are presented and discussed. Likewise, existing DA methods for one- and multidimensional UT data, feature space augmentation, and GANs for augmentation are presented and discussed. The paper closes with an in-detail discussion of the advantages and limitations of existing methods for both synthetic UT training data generation and DA of UT data to aid the decision-making of the reader for the application to specific test cases.
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Affiliation(s)
- Sebastian Uhlig
- Fraunhofer Institute for Ceramic Technologies and Systems, IKTS, Dresden, Germany; Fraunhofer IKTS Cognitive Material Diagnostics Project Group, KogMat(D), Cottbus, Germany
| | - Ilkin Alkhasli
- Fraunhofer Institute for Ceramic Technologies and Systems, IKTS, Dresden, Germany; Fraunhofer IKTS Cognitive Material Diagnostics Project Group, KogMat(D), Cottbus, Germany
| | - Frank Schubert
- Fraunhofer Institute for Ceramic Technologies and Systems, IKTS, Dresden, Germany
| | - Constanze Tschöpe
- Fraunhofer Institute for Ceramic Technologies and Systems, IKTS, Dresden, Germany; Fraunhofer IKTS Cognitive Material Diagnostics Project Group, KogMat(D), Cottbus, Germany
| | - Matthias Wolff
- Brandenburg University of Technology Cottbus-Senftenberg, BTU C-S, Chair of Communications Engineering, Cottbus, Germany.
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