Compressive sensing for efficient health monitoring and effective damage detection of structures

Jayawardhana, M; Zhu, X; Liyanapathirana, R; Gunawardana, U

Compressive sensing for efficient health monitoring and effective damage detection of structures

Jayawardhana, M Zhu, X

Liyanapathirana, R Gunawardana, U

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Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2017, 84 pp. 414 - 430
Issue Date:: 2017-02-01

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Field	Value	Language
dc.contributor.author	Jayawardhana, M	en_US
dc.contributor.author	Zhu, X https://orcid.org/0000-0001-5083-9320	en_US
dc.contributor.author	Liyanapathirana, R	en_US
dc.contributor.author	Gunawardana, U	en_US
dc.date.available	2020-05-25T19:05:55Z
dc.date.issued	2017-02-01	en_US
dc.identifier.citation	Mechanical Systems and Signal Processing, 2017, 84 pp. 414 - 430	en_US
dc.identifier.issn	0888-3270	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124044
dc.description.abstract	© 2016 Elsevier Ltd Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) – a very recent development – introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.	en_US
dc.relation.ispartof	Mechanical Systems and Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.ymssp.2016.07.027	en_US
dc.subject.classification	Acoustics	en_US
dc.title	Compressive sensing for efficient health monitoring and effective damage detection of structures	en_US
dc.type	Journal Article
utslib.citation.volume	84	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	84	en_US

Abstract:

© 2016 Elsevier Ltd Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) – a very recent development – introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

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http://hdl.handle.net/10453/124044