钢结构螺栓连接松动智能检测及监测技术的研究进展

霍林生; 李宏男; 杨卓栋; 周靖

doi:10.13204/j.gyjzG23080112

钢结构螺栓连接松动智能检测及监测技术的研究进展

doi: 10.13204/j.gyjzG23080112

大连理工大学海岸和近海工程国家重点实验室, 辽宁大连 116024

基金项目:

国家自然科学基金资助项目（52178274）。

详细信息

作者简介:
霍林生,男,1975年出生,博士,教授,lshuo@dlut.edu.cn。

通讯作者:
李宏男,hnli@dlut.edu.cn。

计量
- 文章访问数: 282
- HTML全文浏览量: 274
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-01
- 网络出版日期: 2023-11-08

Research Advances of Intelligent Detection and Monitoring Techniques for Loosening of Steel Structure Bolted Connections

State Key Laboratory of Costal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China

摘要

摘要: 螺栓连接是钢结构中最常见的连接形式，其松动和脱落会使得结构的承载能力降低，导致结构产生安全隐患。为避免工程事故的发生，近年来研究人员提出了许多螺栓松动的检测方法。文章综述了一些常见的螺栓松动检测技术的工作原理及其研究进展，包括压电传感技术、光纤传感技术、数字图像处理技术、敲击声法，并对未来螺栓失效检测技术的发展方向进行了探讨。
- 螺栓松动 /
- 无损检测 /
- 传感技术 /
- 数字图像法 /
- 敲击声法
Abstract: The bolted connection is the most common type of connections in steel structures. Loosening and detachment of bolts can reduce the bearing capacity of the structure and lead to safety hazards. In order to prevent engineering accidents, researchers have proposed many methods of detecting bolt loosening in recent years. The paper reviewed the working principles and research advances of some commonly used bolt loosening detection techniques, including piezoelectric sensing technology, fiber optic sensing technology, digital image processing technology, and percussion acoustic method. The future development direction of bolt failure detection technology was also discussed.
- bolt loosening /
- nondestructive detection /
- sensing technology /
- digital image method /
- percussion acoustic method

HTML全文

参考文献(66)

[1]	CHEN D D, SHEN Z H, FU R L, et al. Coda wave interferometry-based very early stage bolt looseness monitoring using a single piezoceramic transducer[J/OL]. Smart Materials and Structures, 2022,31(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/ac5128/meta.
[2]	霍林生, 张晨晨, 赵楠. 基于压电陶瓷的销轴连接受力状态监测[J]. 压电与声光,2022,44(1):48-52.
[3]	陈国达, 郗枫飞, 计时鸣, 等. 螺栓无损检测方法综述[J]. 制造技术与机床, 2017,43(11):22-28.
[4]	霍林生, 田树晓, 王靖凯, 等. 高强螺栓应力锈蚀的声发射特征[J]. 无损检测, 2022,44(1):42-48.
[5]	郭珍珠, 赵伟, 陈涵深, 等. 基于图像识别的高强度螺栓松动检测方法的研究[J]. 工业建筑, 2022,52(2):175-179 ,195.
[6]	盛永军. 采用高强度螺栓连接件的钢-混凝土组合梁力学性能研究[J]. 工业建筑, 2021,51(9):121-126.
[7]	张天雄, 王元清, 陈志华, 等. 不锈钢高强度螺栓机械性能与施拧方法试验研究[J]. 工业建筑, 2022,52(7):98-105 ,97.
[8]	赵海, 胡冰. 风电机组高强螺栓断裂原因分析[J]. 风能, 2018,23(3):70-74.
[9]	陈栩坤. 轴流式风机叶轮脱落事故原因分析及改进措施[J]. 山东工业技术, 2016,12(22):36-45.
[10]	王国印. 大型磨机筒节联接螺栓断裂事故的分析处理[J]. 中国战略新兴产业, 2018,10(32):207-211.
[11]	罗国权. 螺栓疲劳破坏分析[J]. 建筑结构, 2009,39(增刊2):326-328.
[12]	HUO L S, CHEN D D, KONG Q Z, et al. Smart washer:a piezoceramic-based transducer to monitor looseness of bolted connection[J/OL]. Smart Materials and Structures, 2017,26(2)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/26/2/025033/meta.
[13]	HUO L S, CHEN D D, LIANG Y B, et al. Impedance based bolt pre-load monitoring using piezoceramic smart washer[J/OL]. Smart Materials and Structures, 2017,26(5)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/aa6a8e/meta.
[14]	WANG B, HUO L S, CHEN D D, et al. Impedance-based pre-stress monitoring of rock bolts using a piezoceramic-based smart washer:a feasibility study[J]. Sensors, 2017,17(2):21-30.
[15]	PARVASI S M, HO S C M, KONG Q, et al. Real time bolt preload monitoring using piezoceramic transducers and time reversal technique:a numerical study with experimental verification[J/OL]. Smart Materials and Structures, 2016,25(8)[2023-08-01].https://iopscience.iop.org/article/10.1088/0964-1726/25/8/085015.
[16]	HUO L, WANG F, LI H, et al. A fractal contact theory based model for bolted connection looseness monitoring using piezoceramic transducers[J/OL]. Smart Materials and Structures, 2017,26(10)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/aa6e93/meta.
[17]	RUAN J, ZHANG Z, WANG T, et al. An anti-noise real-time cross-correlation method for bolted joint monitoring using piezoceramic transducers[J]. Smart Structures and Systems, 2015,16(2):281-294.
[18]	CAI J, SHI L, YUAN S, et al. High spatial resolution imaging for structural health monitoring based on virtual time reversal[J/OL]. Smart Materials and Structures, 2011,20(5)[2023-08-01].https://iopscience.iop.org/article/10.1088/0964-1726/20/5/055018/meta.
[19]	WU G N, XU C, DU F, et al. A modified time reversal method for guided wave detection of bolt loosening in simulated thermal protection system panels[J/OL]. Complexity, 2018[2023-08-01].https://doi.org/10.1155/2018/8210817.
[20]	DU F, XU C, ZHANG J. A bolt preload monitoring method based on the refocusing capability of virtual time reversal[J/OL]. Structural Control and Health Monitoring, 2019,26(8)[2023-08-01].http://dx.chinadoi.cn/10.1002/stc.2370.
[21]	ZHANG Z, LIU M, SU Z, et al. Quantitative evaluation of residual torque of a loose bolt based on wave energy dissipation and vibro-acoustic modulation:a comparative study[J]. Journal of Sound and Vibration, 2016,383:156-170.
[22]	WANG F, SONG G. Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal[J]. Mechanical Systems and Signal Processing, 2019,130:349-360.
[23]	ZHAO N, HUO L, SONG G. A nonlinear ultrasonic method for real-time bolt looseness monitoring using PZT transducer-enabled vibro-acoustic modulation[J]. Journal of Intelligent Material Systems and Structures, 2020,31(3):364-376.
[24]	ZHAO N, HUO L S, SONG G B. Vibration acoustic modulation for bolt looseness monitoring based on frequency-swept excitation and bispectrum[J/OL]. Smart Materials and Structures, 2023,32(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/acb579.
[25]	丁杰城, 王会平, 袁博, 等. 能量熵与支持向量机融合导波螺栓预紧力识别[J]. 计算机集成制造系统,2022,28(4):1001-1010.
[26]	姜世宇, 徐菁, 任师训, 等. 基于压电阻抗和卷积神经网络的螺栓球节点健康监测[J]. 青岛理工大学学报, 2023,44(2):43-50.
[27]	王琳涛, 张先连, 刘检华, 等. 机理与数据融合的螺栓连接松脱预测[J].计算机集成制造系统,2021,27(3):692-700.
[28]	PAVELKO I, PAVELKO V, KUZNETSOV S, et al. Bolt-joint structural health monitoring by the method of electromechanical impedance[J]. Aircraft Engineering and Aerospace Technology, 2014,86(3):207-214.
[29]	WANG F, SONG G. A novel percussion-based method for multi-bolt looseness detection using one-dimensional memory augmented convolutional long short-term memory networks[J/OL]. Mechanical Systems and Signal Processing, 2021,161[2023-08-01].https://doi.org/10.1016/j.ymssp.2021.107955.
[30]	ZHOU L, CHEN S X, NI Y Q, et al. EMI-GCN:a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks[J/OL]. Smart Materials and Structures, 2021,30(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/abe292.
[31]	DU F, WU S W, XU C, et al. Electromechanical impedance temperature compensation and bolt loosening monitoring based on modified unet and multitask learning[J]. IEEE Sensors Journal, 2023,23(5):4556-4567.
[32]	张佰真铭, 籍长志, 王刚, 等. 光纤健康监测方法在土木工程中的应用进展[J]. 消防界(电子版), 2016,8(4):88-89.
[33]	雷雨恒, 任亮, 李宏男, 等. 基于逆有限元法的形状还原传感阵列及桥梁挠度监测试验[J]. 工程力学,2022,39(11):89-96.
[34]	雷雨恒, 任亮, 刘强平, 等. 基于应变响应反演技术的弓网动态接触力监测方法及应用研究[J]. 机械强度,2022,44(6):1300-1306.
[35]	DUAN C, ZHANG H Y, LI Z S, et al. FBG smart bolts and their application in power grids[J]. IEEE Transactions on Instrumentation and Measurement, 2020,69(5):2515-2521.
[36]	DENG S H, WANG T, TAN B H, et al. Theoretical and experimental study on FBG bending sensor for quantitative monitoring of bolt looseness[J/OL]. Measurement, 2023,216[2023-08-01].https://doi.org/10.1016/j.measurement.2023.113002.
[37]	KHOMENKO A, KORICHO E G, HAQ M, et al. Bolt tension monitoring with reusable fiber Bragg-grating sensors[J]. Journal of Strain Analysis for Engineering Design, 2016,51(2):101-108.
[38]	REN L, FENG T Z, HO M, et al. A smart "shear sensing" bolt based on FBG sensors[J]. Measurement, 2018,122:240-246.
[39]	师琪, 任亮, 尤润州, 等. 基于光纤光栅传感器的智能螺栓开发及应用[J]. 仪表技术与传感器, 2020,455(12):10-15.
[40]	CHEN D D, HUO L S, LI H N, et al. A fiber bragg grating (FBG)-enabled smart washer for bolt pre-load measurement:design, analysis, calibration, and experimental validation[J/OL]. Sensors, 2018,18(8)[2023-08-01].https://www.mdpi.com/1424-8220/18/8/2586.
[41]	DONG C Z, CATBAS F N. A review of computer vision-based structural health monitoring at local and global levels[J]. Structural Health Monitoring, 2021,20(2):692-743.
[42]	YE X W, DONG C Z, LIU T. A review of machine vision-based structural health monitoring:methodologies and applications[J/OL]. Journal of Sensors, 2016[2023-08-01].https://doi.org/10.1155/2016/7103039.
[43]	RAMANA L, CHOI W, CHA Y J. Fully automated vision-based loosened bolt detection using the Viola-Jones algorithm[J]. Structural Health Monitoring, 2019,18(2):422-434.
[44]	CHA Y J, YOU K, CHOI W. Vision-based detection of loosened bolts using the hough transform and support vector machines[J]. Automation in Construction, 2016,71:181-188.
[45]	王宝丽, 于龙. 中低速磁浮F轨紧固螺栓松动故障检测[J]. 信息技术, 2019,43(8):88-92 ,97.
[46]	PARK J H, HUYNH T C, CHOI S H, et al. Vision-based technique for bolt-loosening detection in wind turbine tower[J]. Wind and Structures, 2015,21(6):709-726.
[47]	周靖, 刘煜, 霍林生. 基于机器视觉的螺栓松动旋转角度测量[J]. 机械设计与研究, 2021,37(4):159-162 ,175.
[48]	ZHAO X F, ZHANG Y, WANG N N. Bolt loosening angle detection technology using deep learning[J/OL]. Structural Control and Health Monitoring, 2019,26(1)[2023-08-01].http://dx.chinadoi.cn/10.1002/stc.2292.
[49]	PAN X, YANG T Y. Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking[J]. Computer-Aided Civil and Infrastructure Engineering, 2022,37(10):1207-1222.
[50]	LAO W L, CUI C, ZHANG D K, et al. Computer vision-based autonomous method for quantitative detection of loose bolts in bolted connections of steel structures[J/OL]. Structural Control and Health Monitoring, 2023[2023-08-01].https://doi.org/10.1155/2023/8817058.
[51]	ZHANG Y, YUEN K V. Bolt damage identification based on orientation-aware center point estimation network[J]. Structural Health Monitoring-an International Journal, 2022,21(2):438-450.
[52]	WANG S X, WU J, ZHAO Z, et al. Bolt loosening angle detection based on binocular vision[J/OL]. Measurement Science and Technology, 2023,34(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-6501/aca218/meta.
[53]	PAN X, TAVASOLI S, YANG T Y. Autonomous 3D vision-based bolt loosening assessment using micro aerial vehicles[J]. Computer-Aided Civil and Infrastructure Engineering, 2023:1-12.
[54]	LI Z J, ADAMU K, YAN K, et al. Detection of nut-bolt loss in steel bridges using deep learning techniques[J/OL]. Sustainability, 2022,14(17)[2023-08-01].https://www.mdpi.com/2071-1050/14/17/10837.
[55]	赵欣欣, 钱胜胜, 刘晓光. 基于卷积神经网络的铁路桥梁高强螺栓缺失图像识别方法[J]. 中国铁道科学, 2018,39(4):56-62.
[56]	ZHOU J, HUO L S. Computer vision-based detection for delayed fracture of bolts in steel bridges[J/OL]. Journal of Sensors, 2021[2023-08-01].https://doi.org/10.1155/2021/8325398.
[57]	YANG Z L, ZHAO Y D, XU C. Detection of missing bolts for engineering structures in natural environment using machine vision and deep learning[J/OL]. Sensors, 2023,23(12)[2023-08-01].https://doi.org/10.3390/s23125655.
[58]	陈欣瑞, 周洋, 赵屹涛, 等. 基于改进YOLOv5的移动端螺栓缺失检测方法[J]. 现代制造工程, 2022(11):108-114,143.
[59]	KONG Q, ZHU J, HO S C M, et al. Tapping and listening:a new approach to bolt looseness monitoring[J/OL]. Smart Materials and Structures, 2018,27(7)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/aac962/meta.
[60]	YUAN R, LV Y, KONG Q Z, et al. Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and BP neural network[J/OL]. Smart Materials and Structures, 2019,28(12)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/ab3b39/meta.
[61]	WANG F R, HO S C M, SONG G B. Modeling and analysis of an impact-acoustic method for bolt looseness identification[J/OL]. Mechanical Systems and Signal Processing, 2019,133[2023-08-01]. https://doi.org/10.1016/j.ymssp.2019.106249.
[62]	ZHANG Y, ZHAO X, SUN X, et al. Bolt loosening detection based on audio classification[J]. Advances in Structural Engineering, 2019,22(13):2882-2891.
[63]	YANG Z, HUO L. Bolt preload monitoring based on percussion sound signal and convolutional neural network (CNN)[J]. Nondestructive Testing and Evaluation, 2022,37(4):464-481.
[64]	LIU P, WANG X, CHEN T, et al. Research on a percussion-based bolt looseness identification method based on phase feature and convolutional neural network[J/OL]. Smart Materials and Structures, 2023,32(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/acb4cb/meta.
[65]	YUAN R, LV Y, XU S, et al. ResNet-integrated very early bolt looseness monitoring based on intrinsic feature extraction of percussion sounds[J/OL]. Smart Materials and Structures, 2023,32(3)[2023-08-01].https://iopscience.iop.org/article/10.1088/1361-665X/acb2a0/meta.
[66]	YUAN C, WANG S, QI Y, et al. Automated structural bolt looseness detection using deep learning-based prediction model[J/OL]. Structural Control and Health Monitoring, 2022,29(3)[2023-08-01].https://doi.org/10.1002/stc.2899.