基于视频的机械装备全场振动测量研究

陈立群; 杨天智

doi:10.6052/1000-0992-21-012

基于视频的机械装备全场振动测量研究

doi: 10.6052/1000-0992-21-012

陈立群^1, ,,
杨天智²

1.
哈尔滨工业大学(深圳)理学院力学系, 广东深圳 518055
2.
东北大学机械学院, 沈阳 110819

基金项目: 国家自然科学基金资助项目(11772181)

详细信息

作者简介:
陈立群, 哈尔滨工业大学(深圳)理学院力学系教授, 博士生导师. 从事工程结构的非线性设计与调控、轴向运动连续体振动分析与控制等研究. 曾获国家自然科学奖二等奖, 入选国家杰出青年科学基金、教育部长江学者特聘教授和国家“万人计划”教学名师等人才计划. 享受政府特殊贡献津贴, 被表彰为全国优秀博士后、全国模范教授和全国先进工作者

通讯作者:
chenliqun@hit.edu.cn

中图分类号: O313.2
计量
- 文章访问数: 961
- HTML全文浏览量: 270
- PDF下载量: 181
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-20
- 录用日期: 2021-06-08
- 网络出版日期: 2021-06-18
- 刊出日期: 2021-06-25

Full-field video-based vibration measurement of mechanical equipment

CHEN Liqun^{1
, ,},
YANG Tianzhi²

1.
School of Science, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China
2.
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

More Information

Corresponding author: chenliqun@hit.edu.cn

摘要

摘要: 本文概述了基于视频的机械装备全场振动测量的研究动态和应用前景. 重点讨论了该技术在两个场景中的独特优势: 大尺寸和处于运动叠加中的装备的振动测量问题, 总结了近几年的最新进展和挑战, 最后简述了该方向的发展趋势.
- 非接触全场振动测量 /
- 大尺寸装备 /
- 运动叠加
Abstract: This paper summarizes the trends and application of full-field vibration measurement based on video-based methodology. It focuses on the unique advantages of the two scenarios: vibration measurement of large-size and moving equipment. We summarize the latest progress and challenges in recent years, and finally, briefly present the outlook in this field.
- non-contact full-field vibration measurement /
- large-scale equipment /
- motion overlapping

HTML全文

图 1 机器人辅助激光扫描测振平台

下载: 全尺寸图片幻灯片

图 2 德国Polytec公司的机器人扫描测振平台

下载: 全尺寸图片幻灯片

map

参考文献(11)

[1]	Chen J G, Adams T M, Sun H, et al. 2018. Camera-based vibration measurement of the World War I Memorial Bridge in Portsmouth, New Hampshire. Journal of Structural Engineering, 144: 04018207. doi: 10.1061/(ASCE)ST.1943-541X.0002203
[2]	Durand-Texte T, Simonetto E, Durand S, et al. 2019. Vibration measurement using a pseudo-stereo system, target tracking and vision methods. Mechanical Systems and Signal Processing, 118: 30-40. doi: 10.1016/j.ymssp.2018.08.049
[3]	Gibson J J, 1950. The Perception of the Visual World. Houghton Mifflin.
[4]	Molina-Viedma Á J, López-Alba E, Felipe-Sesé L, et al. 2018. Modal parameters evaluation in a full-scale aircraft demonstrator under different environmental conditions using HS 3D-DIC. Materials, 11: 230. doi: 10.3390/ma11020230
[5]	Rothberg S J, Allen M S, Castellini P, et al. 2017. An international review of laser Doppler vibrometry: Making light work of vibration measurement. Optics and Lasers in Engineering, 99: 11-22. doi: 10.1016/j.optlaseng.2016.10.023
[6]	Sarrafi A, Mao Z, Niezrecki C, et al. 2018. Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification. Journal of Sound and Vibration, 421: 300-318. doi: 10.1016/j.jsv.2018.01.050
[7]	Sun K H, Huh H, Tama B A, et al. 2020. Vision-based fault diagnostics using explainable deep learning with class activation maps. IEEE Access, 8: 129169-129179. doi: 10.1109/ACCESS.2020.3009852
[8]	Xu Y, Brownjohn J M W. 2018. Review of machine-vision based methodologies for displacement measurement in civil structures. J Civil Struct Health Monit, 8: 91-110. doi: 10.1007/s13349-017-0261-4
[9]	Yang Y C, Dorn C, Farrar C, et al. 2020. Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements. Engineering Structures, 207: 110183. doi: 10.1016/j.engstruct.2020.110183
[10]	Yang R, Singh S K, Tavakkoli M, et al. 2020. CNN-LSTM deep learning architecture for computer vision-based modal frequency detection. Mechanical Systems and Signal Processing, 144: 106885. doi: 10.1016/j.ymssp.2020.106885
[11]	Zona A. 2021. Vision-based vibration monitoring of structures and infrastructures: An overview of recent applications. Infrastructures, 6: 4.