Optimization of Damage Features Contaminated by Nonstationary Colored Noise Algorithms using Johansen Cointegration

Hassani, S; Dackermann, U; Gandomi, AH

Optimization of Damage Features Contaminated by Nonstationary Colored Noise Algorithms using Johansen Cointegration

Hassani, S Dackermann, U Gandomi, AH

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Publisher:: DEStech Publications
Publication Type:: Conference Proceeding
Citation:: Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring, 2023, pp. 1604-1613
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Hassani, S
dc.contributor.author	Dackermann, U
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-05-13T12:56:40Z
dc.date.available	2024-05-13T12:56:40Z
dc.date.issued	2023-01-01
dc.identifier.citation	Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring, 2023, pp. 1604-1613
dc.identifier.isbn	9781605956930
dc.identifier.uri	http://hdl.handle.net/10453/178951
dc.description.abstract	For structural health monitoring (SHM), researchers have primarily investigated the effects of signals contaminated with stationary white noise, with very few studies examining pollution caused by highly correlated nonstationary colored noise, such as Brown noise. To address this issue, this paper proposes an optimization-based damage detection technique for composite structures exposed to nonstationary colored noise using condensed frequency response functions (CFRF) as damage-sensitive features (DSF). Two different signal-to-noise ratios (SNR) of Brownian motions, i.e., 20 and 10, are used in the investigation to contaminate CFRFs. Contamination produces nonstationary patterns, making it difficult to detect damage with vibration-driven methods. In this study, we propose a new goal function based on Johansen cointegration, an econometric concept. The proposed objective function converts nonstationary CFRF signals into stationary representations, subsequently fed into optimization-based model updating algorithms. The Reptile Search Algorithm (RSA) is employed to update unknown structural damage indices based on the constructed objective function. The new method is validated on a finite element (FE) model simulating composite laminates with different ply orientations. By comparing the proposed method to a damage detection approach in the literature, the superiority of the proposed method is demonstrated.
dc.language	en
dc.publisher	DEStech Publications
dc.relation.ispartof	Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
dc.relation.ispartof	Proceedings of the 14th International Workshop on Structural Health Monitoring
dc.relation.isbasedon	10.12783/shm2023/36910
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Optimization of Damage Features Contaminated by Nonstationary Colored Noise Algorithms using Johansen Cointegration
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	2024-05-13T12:56:38Z
pubs.publication-status	Published

Abstract:

For structural health monitoring (SHM), researchers have primarily investigated the effects of signals contaminated with stationary white noise, with very few studies examining pollution caused by highly correlated nonstationary colored noise, such as Brown noise. To address this issue, this paper proposes an optimization-based damage detection technique for composite structures exposed to nonstationary colored noise using condensed frequency response functions (CFRF) as damage-sensitive features (DSF). Two different signal-to-noise ratios (SNR) of Brownian motions, i.e., 20 and 10, are used in the investigation to contaminate CFRFs. Contamination produces nonstationary patterns, making it difficult to detect damage with vibration-driven methods. In this study, we propose a new goal function based on Johansen cointegration, an econometric concept. The proposed objective function converts nonstationary CFRF signals into stationary representations, subsequently fed into optimization-based model updating algorithms. The Reptile Search Algorithm (RSA) is employed to update unknown structural damage indices based on the constructed objective function. The new method is validated on a finite element (FE) model simulating composite laminates with different ply orientations. By comparing the proposed method to a damage detection approach in the literature, the superiority of the proposed method is demonstrated.

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http://hdl.handle.net/10453/178951