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• W4313361633 endingPage "384" @default.
• W4313361633 startingPage "384" @default.
• W4313361633 abstract "In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems." @default.
• W4313361633 created "2023-01-06" @default.
• W4313361633 creator A5034515102 @default.
• W4313361633 creator A5067299698 @default.
• W4313361633 date "2022-12-29" @default.
• W4313361633 modified "2023-10-06" @default.
• W4313361633 title "Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing" @default.
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• W4313361633 doi "https://doi.org/10.3390/s23010384" @default.
• W4313361633 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36616982" @default.
• W4313361633 hasPublicationYear "2022" @default.
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