Unsupervised Condition Monitoring of Structures Using VMD and Isolation Forest

Mousavi, M; Gandomi, AH

Unsupervised Condition Monitoring of Structures Using VMD and Isolation Forest

Mousavi, M

Gandomi, AH

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Publisher:: DEStech Publications
Publication Type:: Conference Proceeding
Citation:: Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021, 2021, pp. 550-557
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Mousavi, M https://orcid.org/0000-0002-0436-5176
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-05T12:43:30Z
dc.date.available	2023-03-05T12:43:30Z
dc.date.issued	2021-01-01
dc.identifier.citation	Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021, 2021, pp. 550-557
dc.identifier.isbn	9781605956879
dc.identifier.uri	http://hdl.handle.net/10453/166669
dc.description.abstract	In this paper, an unsupervised condition monitoring of civil infrastructures under environmental and operational variations is proposed. To this end, a couple of the lowest natural frequency signals of the structure recorded over a long period of time are studied for damage. Most of the existing techniques are supervised and require baseline information from the healthy state of the structure. In this paper, however, we explore the possibility of performing unsupervised condition monitoring using Isolation Forest (IF) as a well-known unsupervised machine learning approach. We show that a preprocessing of the frequency signals using the Variational Mode Decomposition (VMD) algorithm plays a key role in the success of the IF algorithm in the unsupervised condition monitoring of structures. To investigate the performance of the proposed method, the benchmark problem of the Z24 bridge is studied in this paper. The results show that the proposed methodology stays successful in most of the cases but at a period of very cold temperature where a nonlinear relationship between the frequencies and temperature presents.
dc.language	en
dc.publisher	DEStech Publications
dc.relation.ispartof	Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021
dc.relation.ispartof	Proceedings of the 13th International Workshop on Structural Health Monitoring
dc.relation.isbasedon	10.12783/shm2021/36300
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Unsupervised Condition Monitoring of Structures Using VMD and Isolation Forest
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-05T12:43:29Z
pubs.publication-status	Published

Abstract:

In this paper, an unsupervised condition monitoring of civil infrastructures under environmental and operational variations is proposed. To this end, a couple of the lowest natural frequency signals of the structure recorded over a long period of time are studied for damage. Most of the existing techniques are supervised and require baseline information from the healthy state of the structure. In this paper, however, we explore the possibility of performing unsupervised condition monitoring using Isolation Forest (IF) as a well-known unsupervised machine learning approach. We show that a preprocessing of the frequency signals using the Variational Mode Decomposition (VMD) algorithm plays a key role in the success of the IF algorithm in the unsupervised condition monitoring of structures. To investigate the performance of the proposed method, the benchmark problem of the Z24 bridge is studied in this paper. The results show that the proposed methodology stays successful in most of the cases but at a period of very cold temperature where a nonlinear relationship between the frequencies and temperature presents.

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http://hdl.handle.net/10453/166669