通过相位投影小波去噪全场提取微妙的位移成分,用于基于视觉的振动测量

IF 10.2 1区 工程技术 Q1 ENGINEERING, MECHANICAL Mechanical Systems and Signal Processing Pub Date : 2025-01-01 Epub Date: 2024-10-11 DOI:10.1016/j.ymssp.2024.112021
Miaoshuo Li , Shixi Yang , Jun He , Xiwen Gu , Yongjia Xu , Fengshou Gu , Andrew D. Ball
{"title":"通过相位投影小波去噪全场提取微妙的位移成分,用于基于视觉的振动测量","authors":"Miaoshuo Li ,&nbsp;Shixi Yang ,&nbsp;Jun He ,&nbsp;Xiwen Gu ,&nbsp;Yongjia Xu ,&nbsp;Fengshou Gu ,&nbsp;Andrew D. Ball","doi":"10.1016/j.ymssp.2024.112021","DOIUrl":null,"url":null,"abstract":"<div><div>While vision-based methods are renowned for their ability in full-field vibration measurements, accurately and robustly extracting subtle displacements remains a significant challenge. To address this, this paper presents a novel Optimal Phase-projection Wavelet Denoising (OPWD) method for vision-based vibration measurement that is adept at extracting characteristics of subtle displacement components. The OPWD method enhances signal quality through a structured three-step process: constructing a signal model from pixel array data, transforming this model into the frequency-space domain, and applying wavelet denoising in the spatial dimension. The method was validated through experimental comparisons on a structural beam, confirming consistency with the resonance frequencies obtained from accelerometers and mode shapes from finite element analysis. This study also contributes a comprehensive framework that lays the groundwork for future developments and implementations of additional methods in vision-based vibration measurement.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112021"},"PeriodicalIF":10.2000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Full-field extraction of subtle displacement components via phase-projection wavelet denoising for vision-based vibration measurement\",\"authors\":\"Miaoshuo Li ,&nbsp;Shixi Yang ,&nbsp;Jun He ,&nbsp;Xiwen Gu ,&nbsp;Yongjia Xu ,&nbsp;Fengshou Gu ,&nbsp;Andrew D. Ball\",\"doi\":\"10.1016/j.ymssp.2024.112021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>While vision-based methods are renowned for their ability in full-field vibration measurements, accurately and robustly extracting subtle displacements remains a significant challenge. To address this, this paper presents a novel Optimal Phase-projection Wavelet Denoising (OPWD) method for vision-based vibration measurement that is adept at extracting characteristics of subtle displacement components. The OPWD method enhances signal quality through a structured three-step process: constructing a signal model from pixel array data, transforming this model into the frequency-space domain, and applying wavelet denoising in the spatial dimension. The method was validated through experimental comparisons on a structural beam, confirming consistency with the resonance frequencies obtained from accelerometers and mode shapes from finite element analysis. This study also contributes a comprehensive framework that lays the groundwork for future developments and implementations of additional methods in vision-based vibration measurement.</div></div>\",\"PeriodicalId\":51124,\"journal\":{\"name\":\"Mechanical Systems and Signal Processing\",\"volume\":\"224 \",\"pages\":\"Article 112021\"},\"PeriodicalIF\":10.2000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanical Systems and Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0888327024009191\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888327024009191","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

摘要

基于视觉的方法因其在全场振动测量中的能力而闻名,但准确、稳健地提取细微位移仍是一项重大挑战。为了解决这个问题,本文提出了一种新颖的优化相位投影小波去噪(OPWD)方法,用于基于视觉的振动测量,该方法善于提取细微位移成分的特征。OPWD 方法通过结构化的三步流程提高信号质量:从像素阵列数据构建信号模型,将该模型转换到频域,并在空间维度应用小波去噪。该方法通过对结构梁的实验对比进行了验证,确认与加速度计获得的共振频率和有限元分析获得的模态振型一致。这项研究还提供了一个综合框架,为未来开发和实施基于视觉的振动测量的其他方法奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Full-field extraction of subtle displacement components via phase-projection wavelet denoising for vision-based vibration measurement
While vision-based methods are renowned for their ability in full-field vibration measurements, accurately and robustly extracting subtle displacements remains a significant challenge. To address this, this paper presents a novel Optimal Phase-projection Wavelet Denoising (OPWD) method for vision-based vibration measurement that is adept at extracting characteristics of subtle displacement components. The OPWD method enhances signal quality through a structured three-step process: constructing a signal model from pixel array data, transforming this model into the frequency-space domain, and applying wavelet denoising in the spatial dimension. The method was validated through experimental comparisons on a structural beam, confirming consistency with the resonance frequencies obtained from accelerometers and mode shapes from finite element analysis. This study also contributes a comprehensive framework that lays the groundwork for future developments and implementations of additional methods in vision-based vibration measurement.
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
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
期刊最新文献
Compound elastic concentrator for achromatic superfocusing and amplified energy harvesting over an ultra-broadband spectrum Stability analysis and multi-sensor placement optimization for dynamic load identification using the Kalman filter method Efficient prediction of extreme buffeting responses of an offshore long-span HSR bridge considering multi-dimensional mean wind uncertainty via a novel PEM-Kriging hybrid strategy Nonlinear viscoelastic isolation performance of a quasi-zero-stiffness flywheel isolator Robust fuzzy control for in-wheel motor drive vehicle nonlinear suspensions under denial-of-service attacks based on dynamic output feedback
×
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学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
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