Experimental study on monitoring the bolt group looseness in a clamping support structure model
In this article, bolt group looseness monitoring in a clamping support structure model, which is under environmental random excitation, is experimentally investigated. The bolt group has 12 bolts, providing an essential clamping force to fix cargo during transportation. Seven kinds of bolt group loo...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2017-03-01
|
Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814017695046 |
id |
doaj-b331754fb90e458e97f1f5c208295217 |
---|---|
record_format |
Article |
spelling |
doaj-b331754fb90e458e97f1f5c2082952172020-11-25T03:44:32ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-03-01910.1177/1687814017695046Experimental study on monitoring the bolt group looseness in a clamping support structure modelGuangming Dong0Fagang Zhao1Xiaoke Zhang2Institute of Vibration, Shock & Noise, Shanghai Jiao Tong University, Shanghai, ChinaShanghai Institute of Satellite Engineering, Shanghai, ChinaShanghai Institute of Satellite Engineering, Shanghai, ChinaIn this article, bolt group looseness monitoring in a clamping support structure model, which is under environmental random excitation, is experimentally investigated. The bolt group has 12 bolts, providing an essential clamping force to fix cargo during transportation. Seven kinds of bolt group looseness degree developed gradually are simulated in experiment. From an economic point of view, vibration transducer of accelerometer is used for monitoring. Time series analysis of Auto-Regressive with eXogenous model with input known and Auto-Regressive–Auto-Regressive with eXogenous model with input unknown are constructed, and the statistical indices of the model residual error are defined as the damage characteristic parameter indicating the bolt group looseness degree. The experimental analysis shows the effectiveness of the proposed method in monitoring the bolt group looseness at early stage, while the variation of structural resonance frequency can only be used to monitor large bolt group looseness degree. The experimental analysis also shows that under a stationary, broadband random excitation, the Auto-Regressive–Auto-Regressive with eXogenous model has the same bolt group looseness monitoring capability with the Auto-Regressive with eXogenous model.https://doi.org/10.1177/1687814017695046 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangming Dong Fagang Zhao Xiaoke Zhang |
spellingShingle |
Guangming Dong Fagang Zhao Xiaoke Zhang Experimental study on monitoring the bolt group looseness in a clamping support structure model Advances in Mechanical Engineering |
author_facet |
Guangming Dong Fagang Zhao Xiaoke Zhang |
author_sort |
Guangming Dong |
title |
Experimental study on monitoring the bolt group looseness in a clamping support structure model |
title_short |
Experimental study on monitoring the bolt group looseness in a clamping support structure model |
title_full |
Experimental study on monitoring the bolt group looseness in a clamping support structure model |
title_fullStr |
Experimental study on monitoring the bolt group looseness in a clamping support structure model |
title_full_unstemmed |
Experimental study on monitoring the bolt group looseness in a clamping support structure model |
title_sort |
experimental study on monitoring the bolt group looseness in a clamping support structure model |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2017-03-01 |
description |
In this article, bolt group looseness monitoring in a clamping support structure model, which is under environmental random excitation, is experimentally investigated. The bolt group has 12 bolts, providing an essential clamping force to fix cargo during transportation. Seven kinds of bolt group looseness degree developed gradually are simulated in experiment. From an economic point of view, vibration transducer of accelerometer is used for monitoring. Time series analysis of Auto-Regressive with eXogenous model with input known and Auto-Regressive–Auto-Regressive with eXogenous model with input unknown are constructed, and the statistical indices of the model residual error are defined as the damage characteristic parameter indicating the bolt group looseness degree. The experimental analysis shows the effectiveness of the proposed method in monitoring the bolt group looseness at early stage, while the variation of structural resonance frequency can only be used to monitor large bolt group looseness degree. The experimental analysis also shows that under a stationary, broadband random excitation, the Auto-Regressive–Auto-Regressive with eXogenous model has the same bolt group looseness monitoring capability with the Auto-Regressive with eXogenous model. |
url |
https://doi.org/10.1177/1687814017695046 |
work_keys_str_mv |
AT guangmingdong experimentalstudyonmonitoringtheboltgrouploosenessinaclampingsupportstructuremodel AT fagangzhao experimentalstudyonmonitoringtheboltgrouploosenessinaclampingsupportstructuremodel AT xiaokezhang experimentalstudyonmonitoringtheboltgrouploosenessinaclampingsupportstructuremodel |
_version_ |
1724514391003496448 |