Proof-of-concept study of bolt connection status monitoring using fiber Bragg grating curvature sensor

被引:13
作者
Deng, Shaohua [1 ,4 ]
Wang, Tao [1 ]
Tan, Bohai [2 ]
Yu, Wei [3 ]
Lu, Guangtao [1 ]
机构
[1] Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control Technol, Minist Educ, Wuhan 430081, Peoples R China
[2] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan 430081, Peoples R China
[3] Wuhan Univ Sci & Technol, Precis Mfg Inst, Wuhan 430081, Peoples R China
[4] China Three Gorges Univ, Coll Mech & Power Engn, Yichang 443002, Peoples R China
基金
中国国家自然科学基金;
关键词
bolt loosening angle; fiber Bragg grating (FBG); curvature sensor; bolt connection status monitoring;
D O I
10.1088/1361-665X/ac9566
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
For connection status monitoring of bolted joint groups, a novel method with fiber Bragg grating (FBG)-based curvature sensor is proposed. An FBG curvature sensor with an inextensible elastic matrix and an offset grating is adopted and clamped between a fixed support and the monitored bolt in a plane bending status. Based on the shape deformation of the FBG curvature sensor induced by the bolt loosening, the bending curvature change of the elastic matrix is measured by the FBG. By detecting the wavelength shift of the FBG induced by the matrix curvature variation, the relationship between the bolt loosening angle and the wavelength shift of FBG can be obtained, and hence the bolt connection status can be determined by the wavelength shift of FBG. Details of the proposed method are presented, and a surface-bonded FBG curvature sensor is designed, fabricated, and experimentally studied to verify the proposed method, and the parameters on sensing performance are also investigated. Experimental results show that the proposed method can monitor bolt loosening angle with high sensitivity and linear output by adjusting the parameters of the FBG curvature sensor. The looseness angle resolution of the proposed sensor can reach 0.0767 degrees. This paper presents the first attempt to monitor bolt loosening angle using an FBG curvature sensor. With the advantages of high sensitivity and resolution, strong applicability, convenience and reusability for the maintenance of bolts, and ease of forming quasi-distributed sensor networks, the proposed method is able to provide a general solution for the on-line monitoring of bolt connection status for large-span distributed bolted-joint groups.
引用
收藏
页数:9
相关论文
共 53 条
[41]   Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine [J].
Wang, Furui ;
Chen, Zheng ;
Song, Gangbing .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 136
[42]   Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method [J].
Wang, Furui ;
Song, Gangbing .
NONLINEAR DYNAMICS, 2020, 100 (01) :243-254
[43]   Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal [J].
Wang, Furui ;
Song, Gangbing .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 130 (349-360) :349-360
[44]   A Novel Fractal Contact-Electromechanical Impedance Model for Quantitative Monitoring of Bolted Joint Looseness [J].
Wang, Furui ;
Ho, Siu Chun Michael ;
Huo, Linsheng ;
Song, Gangbing .
IEEE ACCESS, 2018, 6 :40212-40220
[45]   A Novel RFID-Based Sensing Method for Low-Cost Bolt Loosening Monitoring [J].
Wu, Jian ;
Cui, Xingmei ;
Xu, Yunpeng .
SENSORS, 2016, 16 (02)
[46]   A Novel Comparative Study of European, Chinese and American Codes on Bolt Tightening Sequence Using Smart Bolts [J].
You, Runzhou ;
Ren, Liang ;
Song, Gangbing .
INTERNATIONAL JOURNAL OF STEEL STRUCTURES, 2020, 20 (03) :910-918
[47]   Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning [J].
Yu, Yabin ;
Liu, Ying ;
Chen, Jiawei ;
Jiang, Dong ;
Zhuang, Zilong ;
Wu, Xiaoli .
SENSORS, 2021, 21 (09)
[48]   Near real-time bolt-loosening detection using mask and region-based convolutional neural network [J].
Yuan, Cheng ;
Chen, Wensu ;
Hao, Hong ;
Kong, Qingzhao .
STRUCTURAL CONTROL & HEALTH MONITORING, 2021, 28 (07)
[49]   Bolt loosening angle detection technology using deep learning [J].
Zhao, Xuefeng ;
Zhang, Yang ;
Wang, Niannian .
STRUCTURAL CONTROL & HEALTH MONITORING, 2019, 26 (01)
[50]   A simple macro-bending loss optical fiber crack sensor for the use over a large displacement range [J].
Zheng, Yong ;
Xiao, Wang ;
Zhu, Zheng-Wei .
OPTICAL FIBER TECHNOLOGY, 2020, 58