A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM
This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2018-01-01
|
Series: | Journal of Sensors |
Online Access: | http://dx.doi.org/10.1155/2018/2107679 |
id |
doaj-f2448bd420f943efb4c2e0bb9e5788c3 |
---|---|
record_format |
Article |
spelling |
doaj-f2448bd420f943efb4c2e0bb9e5788c32020-11-25T00:13:41ZengHindawi LimitedJournal of Sensors1687-725X1687-72682018-01-01201810.1155/2018/21076792107679A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHMNicola Testoni0Cristiano Aguzzi1Valentina Arditi2Federica Zonzini3Luca De Marchi4Alessandro Marzani5Tullio Salmon Cinotti6Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, ItalyDepartment of Computer Science and Engineering, University of Bologna, Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, Bologna, ItalyDepartment of Computer Science and Engineering, University of Bologna, Bologna, ItalyThis work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges.http://dx.doi.org/10.1155/2018/2107679 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nicola Testoni Cristiano Aguzzi Valentina Arditi Federica Zonzini Luca De Marchi Alessandro Marzani Tullio Salmon Cinotti |
spellingShingle |
Nicola Testoni Cristiano Aguzzi Valentina Arditi Federica Zonzini Luca De Marchi Alessandro Marzani Tullio Salmon Cinotti A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM Journal of Sensors |
author_facet |
Nicola Testoni Cristiano Aguzzi Valentina Arditi Federica Zonzini Luca De Marchi Alessandro Marzani Tullio Salmon Cinotti |
author_sort |
Nicola Testoni |
title |
A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM |
title_short |
A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM |
title_full |
A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM |
title_fullStr |
A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM |
title_full_unstemmed |
A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM |
title_sort |
sensor network with embedded data processing and data-to-cloud capabilities for vibration-based real-time shm |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2018-01-01 |
description |
This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges. |
url |
http://dx.doi.org/10.1155/2018/2107679 |
work_keys_str_mv |
AT nicolatestoni asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT cristianoaguzzi asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT valentinaarditi asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT federicazonzini asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT lucademarchi asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT alessandromarzani asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT tulliosalmoncinotti asensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT nicolatestoni sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT cristianoaguzzi sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT valentinaarditi sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT federicazonzini sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT lucademarchi sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT alessandromarzani sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm AT tulliosalmoncinotti sensornetworkwithembeddeddataprocessinganddatatocloudcapabilitiesforvibrationbasedrealtimeshm |
_version_ |
1725393636824186880 |