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核资产的自动化实时电涡流阵列检测。

Automated Real-Time Eddy Current Array Inspection of Nuclear Assets.

机构信息

SEARCH: Sensor Enabled Automation, Robotics & Control Hub, Centre for Ultrasonic Engineering (CUE), Department of Electronic & Electrical Engineering, University of Strathclyde, Royal College Building, 204 George Street, Glasgow G1 1XW, UK.

National Nuclear Laboratory LTD., Warrington WA3 6AE, UK.

出版信息

Sensors (Basel). 2022 Aug 12;22(16):6036. doi: 10.3390/s22166036.

DOI:10.3390/s22166036
PMID:36015795
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9414535/
Abstract

Inspection of components with surface discontinuities is an area that volumetric Non-Destructive Testing (NDT) methods, such as ultrasonic and radiographic, struggle in detection and characterisation. This coupled with the industrial desire to detect surface-breaking defects of components at the point of manufacture and/or maintenance, to increase design lifetime and further embed sustainability in their business models, is driving the increased adoption of Eddy Current Testing (ECT). Moreover, as businesses move toward Industry 4.0, demand for robotic delivery of NDT has grown. In this work, the authors present the novel implementation and use of a flexible robotic cell to deliver an eddy current array to inspect stress corrosion cracking on a nuclear canister made from 1.4404 stainless steel. Three 180-degree scans at different heights on one side of the canister were performed, and the acquired impedance data were vertically stitched together to show the full extent of the cracking. Axial and transversal datasets, corresponding to the transmit/receive coil configurations of the array elements, were simultaneously acquired at transmission frequencies 250, 300, 400, and 450 kHz and allowed for the generation of several impedance C-scan images. The variation in the lift-off of the eddy current array was innovatively minimised through the use of a force-torque sensor, a padded flexible ECT array and a PI control system. Through the use of bespoke software, the impedance data were logged in real-time (≤7 ms), displayed to the user, saved to a binary file, and flexibly post-processed via phase-rotation and mixing of the impedance data of different frequency and coil configuration channels. Phase rotation alone demonstrated an average increase in Signal to Noise Ratio (SNR) of 4.53 decibels across all datasets acquired, while a selective sum and average mixing technique was shown to increase the SNR by an average of 1.19 decibels. The results show how robotic delivery of eddy current arrays, and innovative post-processing, can allow for repeatable and flexible surface inspection, suitable for the challenges faced in many quality-focused industries.

摘要

对具有表面不连续性的部件进行检查是体积无损检测(NDT)方法(如超声波和射线照相)在检测和特征描述方面存在困难的一个领域。再加上工业界希望在制造和/或维护时检测到部件的表面穿透缺陷,以提高设计寿命,并在其商业模式中进一步嵌入可持续性,这推动了电涡流检测(ECT)的广泛应用。此外,随着企业向工业 4.0 迈进,对 NDT 机器人交付的需求也在增长。在这项工作中,作者提出了一种灵活的机器人单元的新颖实现和使用,该单元用于输送电涡流阵列,以检查由 1.4404 不锈钢制成的核容器上的应力腐蚀裂纹。在容器的一侧进行了三次不同高度的 180 度扫描,采集的阻抗数据垂直拼接在一起,以显示裂纹的全部范围。轴向和横向数据集对应于阵列元件的发送/接收线圈配置,在传输频率 250、300、400 和 450 kHz 下同时采集,并允许生成多个阻抗 C 扫描图像。通过使用力-扭矩传感器、带衬垫的柔性 ECT 阵列和 PI 控制系统,创新地最小化了电涡流阵列的提离变化。通过使用定制软件,实时记录阻抗数据(≤7 ms),显示给用户,保存到二进制文件,并通过相位旋转和不同频率和线圈配置通道的阻抗数据混合灵活地进行后处理。仅相位旋转就显示出所有采集数据集的平均信噪比(SNR)提高了 4.53 分贝,而选择性求和平均混合技术显示平均 SNR 提高了 1.19 分贝。结果表明,电涡流阵列的机器人输送和创新的后处理如何能够实现可重复和灵活的表面检查,适用于许多注重质量的行业所面临的挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c5e/9414535/c36fdc0a1e17/sensors-22-06036-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c5e/9414535/d8c1a9efcc18/sensors-22-06036-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c5e/9414535/8817bbbf658d/sensors-22-06036-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c5e/9414535/c36fdc0a1e17/sensors-22-06036-g012.jpg

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本文引用的文献

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2
Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data.基于有限实验数据的超声裂纹特征改进的领域自适应深度学习。
IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Apr;69(4):1485-1496. doi: 10.1109/TUFFC.2022.3151397. Epub 2022 Mar 30.
3
Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE.
Sensors (Basel). 2022 Oct 9;22(19):7654. doi: 10.3390/s22197654.
用于自动焊接和过程中超声无损检测的传感器赋能多机器人系统
Sensors (Basel). 2021 Jul 27;21(15):5077. doi: 10.3390/s21155077.
4
Deep Learning for Ultrasonic Crack Characterization in NDE.用于无损检测中超声裂纹表征的深度学习
IEEE Trans Ultrason Ferroelectr Freq Control. 2021 May;68(5):1854-1865. doi: 10.1109/TUFFC.2020.3045847. Epub 2021 Apr 26.
5
Data Fusion of Multiview Ultrasonic Imaging for Characterization of Large Defects.用于大缺陷表征的多视图超声成像数据融合
IEEE Trans Ultrason Ferroelectr Freq Control. 2020 Nov;67(11):2387-2401. doi: 10.1109/TUFFC.2020.3004982. Epub 2020 Jun 25.
6
A Study of the Automated Eddy Current Detection of Cracks in Steel Plates.钢板裂纹自动涡流检测研究
J Nondestr Eval. 2020;39(1):6. doi: 10.1007/s10921-019-0647-9. Epub 2019 Dec 28.