Structural health monitoring: an advanced signal processing perspective

This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on t...

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Detalles Bibliográficos
Autores principales: Yan, Ruqiang, Chen, Xuefeng, Mukhopadhyay, Subhas
Lenguaje:eng
Publicado: Springer 2017
Materias:
Engineering
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-56126-4
http://cds.cern.ch/record/2262181
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author Yan, Ruqiang
Chen, Xuefeng
Mukhopadhyay, Subhas
author_facet Yan, Ruqiang
Chen, Xuefeng
Mukhopadhyay, Subhas
author_sort Yan, Ruqiang
collection CERN
description This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.
id cern-2262181
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
publisher Springer
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spelling cern-22621812021-04-21T19:15:23Zdoi:10.1007/978-3-319-56126-4http://cds.cern.ch/record/2262181engYan, RuqiangChen, XuefengMukhopadhyay, SubhasStructural health monitoring: an advanced signal processing perspectiveEngineeringThis book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.Springeroai:cds.cern.ch:22621812017
spellingShingle Engineering
Yan, Ruqiang
Chen, Xuefeng
Mukhopadhyay, Subhas
Structural health monitoring: an advanced signal processing perspective
title Structural health monitoring: an advanced signal processing perspective
title_full Structural health monitoring: an advanced signal processing perspective
title_fullStr Structural health monitoring: an advanced signal processing perspective
title_full_unstemmed Structural health monitoring: an advanced signal processing perspective
title_short Structural health monitoring: an advanced signal processing perspective
title_sort structural health monitoring: an advanced signal processing perspective
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-56126-4
http://cds.cern.ch/record/2262181
work_keys_str_mv AT yanruqiang structuralhealthmonitoringanadvancedsignalprocessingperspective
AT chenxuefeng structuralhealthmonitoringanadvancedsignalprocessingperspective
AT mukhopadhyaysubhas structuralhealthmonitoringanadvancedsignalprocessingperspective

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