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dc.contributor.authorWang, Dong*
dc.contributor.authorZhang, Xiancheng*
dc.contributor.authorChen, Gang*
dc.contributor.authorCorreia, José A.F.O.*
dc.contributor.authorQian, Guian*
dc.contributor.authorZhu, Shun-Peng*
dc.date.accessioned2021-02-12T04:42:58Z
dc.date.available2021-02-12T04:42:58Z
dc.date.issued2020*
dc.date.submitted2020-01-30 16:39:46*
dc.identifier43647*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/60148
dc.description.abstractThe idea of preparing an Energies Special Issue on “Structural Prognostics and Health Management in Power & Energy Systems” is to compile information on the recent advances in structural prognostics and health management (SPHM). Continued improvements on SPHM have been made possible through advanced signature analysis, performance degradation assessment, as well as accurate modeling of failure mechanisms by introducing advanced mathematical approaches/tools. Through combining deterministic and probabilistic modeling techniques, research on SPHM can provide assurance for new structures at a design stage and ensure construction integrity at a fabrication phase. Specifically, power and energy system failures occur under multiple sources of uncertainty/variability resulting from load variations in usage, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on SPHM are desired and expected, which attempt to prevent overdesign and unnecessary inspection and provide tools to enable a balance between safety and economy to be achieved. This Special Issue has attracted submissions from China, USA, Portugal, and Italy. A total of 26 submissions were received and 11 articles finally published.*
dc.languageEnglish*
dc.subjectB1-5802*
dc.subject.otherempirical mode decomposition*
dc.subject.otherunderground powerhouse*
dc.subject.othersensitivity analysis*
dc.subject.otherDNN*
dc.subject.otherfault detection*
dc.subject.otherneural networks*
dc.subject.otherstructural health monitoring*
dc.subject.otheranalysis mode decomposition*
dc.subject.otherdynamic analysis of the structure*
dc.subject.otherresidual useful life*
dc.subject.otherrenewable energy*
dc.subject.otherremaining useful life*
dc.subject.otherretrofitting activities*
dc.subject.otherwind turbine blade*
dc.subject.otheroptimized deep belief networks*
dc.subject.otherstrain prediction*
dc.subject.otheroffshore wind turbines*
dc.subject.otherlow frequency tail fluctuation*
dc.subject.otheroil and gas platforms*
dc.subject.othersupporting vector machine (SVM)*
dc.subject.otherwave–structure interaction (WSI)*
dc.subject.othersifting stop criterion*
dc.subject.otherprobabilistic analyses of stochastic processes and frequency*
dc.subject.othermode mixing*
dc.subject.othernon-probabilistic reliability index*
dc.subject.otherdata-driven*
dc.subject.otherprognostics*
dc.subject.otherturbine blisk*
dc.subject.otherwind turbines*
dc.subject.othersupervisory control and data acquisition system*
dc.subject.otherfuzzy safety criterion*
dc.subject.otheranalysis-empirical mode decomposition*
dc.subject.otherrotation of hydraulic generator*
dc.subject.otherlife cycle cost*
dc.subject.otherhealth monitoring*
dc.subject.otherreliability*
dc.subject.otherwavelet decomposition*
dc.subject.otherweighted regression*
dc.subject.othersimilarity-based approach*
dc.subject.othervibration transmission mechanism*
dc.subject.otherwind and wave analysis*
dc.subject.otherfull-scale static test*
dc.subject.otherdeep learning*
dc.subject.othermultioperation condition*
dc.subject.otherextremum surface response method*
dc.subject.otherlithium-ion battery*
dc.subject.othervibration test*
dc.subject.otherlateral-river vibration*
dc.subject.otheroperational modal analysis*
dc.subject.otherdynamic analysis*
dc.subject.otherregeneration phenomenon*
dc.subject.othermachine learning*
dc.subject.otherprognostic and Health Management*
dc.subject.otheroffshore structures*
dc.subject.otherNAR neural network*
dc.subject.othertechno-economic assessments*
dc.subject.otherstochastic subspace identification*
dc.subject.othervertical axis wind turbine*
dc.subject.otherdynamic fuzzy reliability analysis*
dc.titleStructural Prognostics and Health Management in Power & Energy Systems*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-767-0*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
virtual.oapen_relation_isPublishedBy.publisher_nameMDPI - Multidisciplinary Digital Publishing Institute
virtual.oapen_relation_isPublishedBy.publisher_websitewww.mdpi.com/books
oapen.relation.isbn9783039217670*
oapen.relation.isbn9783039217663*
oapen.pages218*
oapen.edition1st*


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