Non-destructive Testing of Steel Wire Ropes Incorporating Magnetic Memory Information

Juwei Zhang, Zengguang Zhang, Bo Liu
{"title":"Non-destructive Testing of Steel Wire Ropes Incorporating Magnetic Memory Information","authors":"Juwei Zhang, Zengguang Zhang, Bo Liu","doi":"10.1784/insi.2023.65.2.87","DOIUrl":null,"url":null,"abstract":"In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition\n rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm\n to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the\n grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insight - Non-Destructive Testing and Condition Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1784/insi.2023.65.2.87","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.
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
含磁记忆信息钢丝绳的无损检测
为避免干扰磁场的影响,构建弱磁场激励下的钢丝绳磁记忆检测平台,将增强磁记忆信号、红外信号和可见光信号融合,提高断线定量识别的识别率,降低断线定量识别的识别误差,实现更有效的钢丝绳缺陷识别和寿命评估。采用内禀时间尺度分解(ITD)和小波算法对磁记忆信号进行降噪,有效地去除信号基线波和链波等噪声。采用基于曲线变换的图像融合方法实现了缺陷图像的像素级融合。将提取的融合图像特征作为输入,通过灰狼优化器(GWO-SVM)神经网络优化支持向量机,定量识别钢丝绳缺陷。结果表明,图像融合方法对断线的识别效果优于单一检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Multi-criterion analysis-based artificial intelligence system for condition monitoring of electrical transformers MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics A multi-frequency balanced electromagnetic field measurement for arbitrary angles of pipeline cracks with high sensitivity Ultrasonic total focusing method for internal defects in composite insulators Developments in ultrasonic and eddy current testing in the 1970s and 1980s with emphasis on the requirements of the UK nuclear power industry
×
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