基于机器学习的储罐金属缺陷智能量化技术

IF 0.9 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI:10.1134/S1061830923600685
Chao Ding, Yuanyuan He, Donglin Tang, Yamei Li, Pingjie Wang, Yunliang Zhao, Sheng Rao, Chao Qin
{"title":"基于机器学习的储罐金属缺陷智能量化技术","authors":"Chao Ding,&nbsp;Yuanyuan He,&nbsp;Donglin Tang,&nbsp;Yamei Li,&nbsp;Pingjie Wang,&nbsp;Yunliang Zhao,&nbsp;Sheng Rao,&nbsp;Chao Qin","doi":"10.1134/S1061830923600685","DOIUrl":null,"url":null,"abstract":"<p>Wall-climbing robot are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. The ultrasonic dry-coupling detection device installed on the wall-climbing robot detects the defects of the tank wall. Aiming at the difficulty that the ultrasonic A-scan signal obtained by the ultrasonic dry-coupling detection method has waveform cross-aliasing, which makes it difficult to obtain effective information in traditional feature extraction, Herein, we combine the fast Fourier transform, wavelet packet decomposition and empirical mode decomposition techniques to propose a 3D-SFE method performs multi-scale feature extraction on dry coupled signals. At the same time, in view of the difficulty that traditional nondestructive testing models cannot quantify the defect area accurately, we introduce the XGBoost model to better quantify the defect area. Our proposed defect area quantification model based on multi-scale feature extraction achieves 99.9% accuracy on the training set and 81.5% on the test set. Furthermore, we also analyzed the influence of defect characteristics, sample number, defect shape and depth on the model, and then provided certain guiding significance for the detection of tank defects.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 12","pages":"1207 - 1222"},"PeriodicalIF":0.9000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Quantification of Metal Defects in Storage Tanks Based on Machine Learning\",\"authors\":\"Chao Ding,&nbsp;Yuanyuan He,&nbsp;Donglin Tang,&nbsp;Yamei Li,&nbsp;Pingjie Wang,&nbsp;Yunliang Zhao,&nbsp;Sheng Rao,&nbsp;Chao Qin\",\"doi\":\"10.1134/S1061830923600685\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Wall-climbing robot are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. The ultrasonic dry-coupling detection device installed on the wall-climbing robot detects the defects of the tank wall. Aiming at the difficulty that the ultrasonic A-scan signal obtained by the ultrasonic dry-coupling detection method has waveform cross-aliasing, which makes it difficult to obtain effective information in traditional feature extraction, Herein, we combine the fast Fourier transform, wavelet packet decomposition and empirical mode decomposition techniques to propose a 3D-SFE method performs multi-scale feature extraction on dry coupled signals. At the same time, in view of the difficulty that traditional nondestructive testing models cannot quantify the defect area accurately, we introduce the XGBoost model to better quantify the defect area. Our proposed defect area quantification model based on multi-scale feature extraction achieves 99.9% accuracy on the training set and 81.5% on the test set. Furthermore, we also analyzed the influence of defect characteristics, sample number, defect shape and depth on the model, and then provided certain guiding significance for the detection of tank defects.</p>\",\"PeriodicalId\":764,\"journal\":{\"name\":\"Russian Journal of Nondestructive Testing\",\"volume\":\"59 12\",\"pages\":\"1207 - 1222\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Russian Journal of Nondestructive Testing\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1061830923600685\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830923600685","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

摘要爬壁机器人越来越多地被用于工业资产的自动远程和现场检测,无需危险的人工进入。爬壁机器人上安装的超声波干耦合检测装置可检测罐壁的缺陷。针对超声干耦合检测方法获得的超声 A-scan 信号存在波形交叉混叠,传统特征提取难以获取有效信息的难题,我们结合快速傅里叶变换、小波包分解和经验模态分解技术,提出了一种对干耦合信号进行多尺度特征提取的 3D-SFE 方法。同时,针对传统无损检测模型无法准确量化缺陷面积的难题,我们引入了 XGBoost 模型来更好地量化缺陷面积。我们提出的基于多尺度特征提取的缺陷面积量化模型在训练集上的准确率达到 99.9%,在测试集上的准确率达到 81.5%。此外,我们还分析了缺陷特征、样本数量、缺陷形状和深度对模型的影响,为坦克缺陷检测提供了一定的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Intelligent Quantification of Metal Defects in Storage Tanks Based on Machine Learning

Wall-climbing robot are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. The ultrasonic dry-coupling detection device installed on the wall-climbing robot detects the defects of the tank wall. Aiming at the difficulty that the ultrasonic A-scan signal obtained by the ultrasonic dry-coupling detection method has waveform cross-aliasing, which makes it difficult to obtain effective information in traditional feature extraction, Herein, we combine the fast Fourier transform, wavelet packet decomposition and empirical mode decomposition techniques to propose a 3D-SFE method performs multi-scale feature extraction on dry coupled signals. At the same time, in view of the difficulty that traditional nondestructive testing models cannot quantify the defect area accurately, we introduce the XGBoost model to better quantify the defect area. Our proposed defect area quantification model based on multi-scale feature extraction achieves 99.9% accuracy on the training set and 81.5% on the test set. Furthermore, we also analyzed the influence of defect characteristics, sample number, defect shape and depth on the model, and then provided certain guiding significance for the detection of tank defects.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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).
期刊最新文献
Laser Ultrasonic Measurements for Generation and Detection of Lateral Waves in a Solid for Surface Defect Inspection Sparse Optimal Design of Ultrasonic Phased Array for Efficient DMAS Imaging Developing a Method for Assessing the Degree of Hydrogenation of VT1-0 Titanium Alloy by the Acoustic Method Layered Composite Hydrogenated Films of Zirconium and Niobium: Production Method and Testing Using Thermo EMF (Thermoelectric Method) Evaluating Efficiency of Foreign Object Detection Technology Based on the Use of Passive Infrared Thermography
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1