1. Passive structural monitoring based on data-driven matched field processing
- Author
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François Baqué, Sandrine T. Rakotonarivo, J.-F. Chaix, William A. Kuperman, Serge Mensah, Emma Lubeigt, Laboratoire de Mécanique et d'Acoustique [Marseille] (LMA ), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Service de Technologie des Composants et des Procédés (STCP), Département Technologie Nucléaire (DTN), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Ondes et Imagerie (O&I), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marine Physical Laboratory (MPL), Scripps Institution of Oceanography (SIO - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), MISTRAL-Lab, Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM), Scripps Institution of Oceanography (SIO), University of California-University of California-University of California [San Diego] (UC San Diego), and University of California-University of California
- Subjects
Acoustics and Ultrasonics ,Field (physics) ,010505 oceanography ,Computer science ,Acoustics ,Ambient noise level ,Condition monitoring ,White noise ,01 natural sciences ,[PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph] ,Vibration ,Minimum-variance unbiased estimator ,Arts and Humanities (miscellaneous) ,Frequency domain ,0103 physical sciences ,010301 acoustics ,Structural acoustics ,ComputingMilieux_MISCELLANEOUS ,0105 earth and related environmental sciences - Abstract
A passive data-driven method to localize a defect in a structure using the ambient noise is derived. The approach requires only acoustic measurements in a spatially random noise field and no knowledge of the structure. Measurements are taken before and after the perturbation has occurred and Green's functions are retrieved by cross-correlating acoustic measurements. The difference between measured data reveals the perturbation. A frequency domain method based on matched field processing is then performed to localize the perturbation. The Bartlett, minimum variance and white noise gain constraint processors are implemented and their performances are illustrated on a numerical experiment.
- Published
- 2019
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