基于超声波、涡流和目视无损检测方法联合诊断结果的管道表面缺陷分类和尺寸确定

IF 0.9 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
N. V. Krysko, S. V. Skrynnikov, N. A. Shchipakov, D. M. Kozlov, A. G. Kusyy
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引用次数: 0

摘要

摘要 根据无损检测的超声波、涡流和目视检测方法的结果,考虑了表面操作缺陷的分类和定性问题。同时,利用配备计算机视觉功能和激光三角测量传感器的电视检测相机实现了视觉检测方法。论文提供了一个数据集,其中包含 5760 幅有点蚀和无点蚀的管道图像。论文介绍了一种卷积神经网络 (CNN),该网络用于将从电视检测相机获得的图像分为无腐蚀图像和有点蚀图像。论文介绍了一个数据集,其中包含 269 个平面和体积表面缺陷的测量值。论文提出了一个基于梯度提升的表面缺陷尺寸模型。论文开发了一种在复杂诊断中应用所获模型进行表面缺陷分类和大小确定的算法,并确定了该算法在 RMSE 指标上的准确性,在所研究的测试数据集中计算出的 RMSE 值为 0.011 毫米。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing

The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm.

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来源期刊
Russian Journal of Nondestructive Testing
Russian Journal of Nondestructive Testing 工程技术-材料科学:表征与测试
CiteScore
1.60
自引率
44.40%
发文量
59
审稿时长
6-12 weeks
期刊介绍: Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
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