Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks

The bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt looseness. Moreover,...

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Main Authors: Jing Xu, Jinhui Dong, Hongnan Li, Chunwei Zhang, Siu Chun Ho
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/8/1906
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spelling doaj-1c8b11620fca4431b885b54c4c467b342020-11-24T21:46:51ZengMDPI AGSensors1424-82202019-04-01198190610.3390/s19081906s19081906Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural NetworksJing Xu0Jinhui Dong1Hongnan Li2Chunwei Zhang3Siu Chun Ho4School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, ChinaSchool of Civil Engineering, Qingdao University of Technology, Qingdao 266033, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Civil Engineering, Qingdao University of Technology, Qingdao 266033, ChinaDepartment of Mechanical Engineering, University of Houston, Houston, TX 77004, USAThe bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt looseness. Moreover, the bolt looseness leads to the reduction of the local stiffness and bearing capacity for the structure. In this regard, this study used the electro-mechanical impedance (EMI) technique and back propagation neural networks (BPNNs) to monitor the bolt looseness inside the BSJ. Therefore, a space grid specimen having bolted spherical joints and tubular bars was considered for experimental evaluation. Different torques levels were applied on the sleeve to represent different looseness degrees of joint connection. As the torque levels increased, the looseness degrees of joint connection increased correspondingly. The lead zirconate titanate (PZT) patch was used and integrated with the tubular bar due to its strong piezoelectric effect. The root-mean-square deviation (RMSD) of the conductance signatures for the PZT patch were used as the looseness-monitoring indexes. Taking RMSD values of sub-frequency bands and the looseness degrees as inputs and outputs respectively, the BPNNs were trained and tested in twenty repeated experiments. The experimental results show that the formation of the bolt looseness can be detected according to the changes of looseness-monitoring indexes, and the degree of bolt looseness by the trained BPNNs. Overall, this research demonstrates that the proposed structural health monitoring (SHM) technique is feasible for monitoring the looseness of bolted spherical connection in space grid structures.https://www.mdpi.com/1424-8220/19/8/1906structural health monitoring (SHM)space grid structureselectro-mechanical impedance (EMI)back propagation neural networks (BPNNs)bolted spherical joint (BSJ)Bolt looseness damage
collection DOAJ
language English
format Article
sources DOAJ
author Jing Xu
Jinhui Dong
Hongnan Li
Chunwei Zhang
Siu Chun Ho
spellingShingle Jing Xu
Jinhui Dong
Hongnan Li
Chunwei Zhang
Siu Chun Ho
Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
Sensors
structural health monitoring (SHM)
space grid structures
electro-mechanical impedance (EMI)
back propagation neural networks (BPNNs)
bolted spherical joint (BSJ)
Bolt looseness damage
author_facet Jing Xu
Jinhui Dong
Hongnan Li
Chunwei Zhang
Siu Chun Ho
author_sort Jing Xu
title Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
title_short Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
title_full Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
title_fullStr Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
title_full_unstemmed Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
title_sort looseness monitoring of bolted spherical joint connection using electro-mechanical impedance technique and bp neural networks
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2019-04-01
description The bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt looseness. Moreover, the bolt looseness leads to the reduction of the local stiffness and bearing capacity for the structure. In this regard, this study used the electro-mechanical impedance (EMI) technique and back propagation neural networks (BPNNs) to monitor the bolt looseness inside the BSJ. Therefore, a space grid specimen having bolted spherical joints and tubular bars was considered for experimental evaluation. Different torques levels were applied on the sleeve to represent different looseness degrees of joint connection. As the torque levels increased, the looseness degrees of joint connection increased correspondingly. The lead zirconate titanate (PZT) patch was used and integrated with the tubular bar due to its strong piezoelectric effect. The root-mean-square deviation (RMSD) of the conductance signatures for the PZT patch were used as the looseness-monitoring indexes. Taking RMSD values of sub-frequency bands and the looseness degrees as inputs and outputs respectively, the BPNNs were trained and tested in twenty repeated experiments. The experimental results show that the formation of the bolt looseness can be detected according to the changes of looseness-monitoring indexes, and the degree of bolt looseness by the trained BPNNs. Overall, this research demonstrates that the proposed structural health monitoring (SHM) technique is feasible for monitoring the looseness of bolted spherical connection in space grid structures.
topic structural health monitoring (SHM)
space grid structures
electro-mechanical impedance (EMI)
back propagation neural networks (BPNNs)
bolted spherical joint (BSJ)
Bolt looseness damage
url https://www.mdpi.com/1424-8220/19/8/1906
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