Structural health monitoring under environmental and operational variations using MCD prediction error

Mousavi, M; Gandomi, AH

Structural health monitoring under environmental and operational variations using MCD prediction error

Mousavi, M

Gandomi, AH

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Sound and Vibration, 2021, 512, pp. 116370
Issue Date:: 2021-11-10

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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	2022-01-21T10:37:26Z
dc.date.available	2022-01-21T10:37:26Z
dc.date.issued	2021-11-10
dc.identifier.citation	Journal of Sound and Vibration, 2021, 512, pp. 116370
dc.identifier.issn	0022-460X
dc.identifier.issn	1095-8568
dc.identifier.uri	http://hdl.handle.net/10453/153423
dc.description.abstract	This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as “bad” outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Sound and Vibration
dc.relation.isbasedon	10.1016/j.jsv.2021.116370
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	Acoustics
dc.title	Structural health monitoring under environmental and operational variations using MCD prediction error
dc.type	Journal Article
utslib.citation.volume	512
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-11-10T00:00:00+1000Z
dc.date.updated	2022-01-21T10:37:24Z
pubs.publication-status	Published
pubs.volume	512

Abstract:

This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as “bad” outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results.

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http://hdl.handle.net/10453/153423