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MACHINE LEARNING-BASED METHODS AND SYSTEMS FOR DEFFECT DETECTION AND ANALYSIS USING ULTRASOUND SCANS

机译:基于机器学习的方法和系统,用于使用超声扫描进行脱近检测和分析

摘要

A technological solution for analyzing a sequence of ultrasound scan images of an asset and diagnosing a health condition of a section of the asset. The solution includes receiving, by a machine learning platform, an ultrasound scan image of the section of the asset; analyzing, by the machine learning platform, the ultrasound scan image to detect any aberrations in the section; generating, by the machine learning platform, an aberration label for each detected aberration in the section; labeling, by the machine learning platform, the section of the asset with a section condition label; and, rendering, by a display device, the section conditional label. The section condition label can be based on each detected aberration in the section. The section condition label can include at least one of an aberration area ratio, a total number of aberrations, and the aberration label for each detected aberration in the section of the asset.
机译:用于分析资产的超声扫描图像序列的技术解决方案,并诊断资产部分的健康状况。 该解决方案包括通过机器学习平台接收资产部分的超声扫描图像; 通过机器学习平台分析超声扫描图像以检测该部分中的任何像差; 通过机器学习平台生成每个检测到的像差的像差标签; 通过机器学习平台标记,资产的部分具有部分条件标签; 并且,通过显示设备渲染部分条件标签。 部分条件标签可以基于该部分中的每个检测到的像差。 截面状况标签可以包括像差区域比,差距的总数和像差标签中的至少一个,每个检测到的像差在资产部分中的像差。

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