LEADER 04944nam 2201093z- 450 001 9910372782603321 005 20210212 010 $a3-03921-767-4 035 $a(CKB)4100000010163798 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/60148 035 $a(oapen)doab60148 035 $a(EXLCZ)994100000010163798 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStructural Prognostics and Health Management in Power & Energy Systems 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (218 p.) 311 08$a3-03921-766-6 330 $aThe 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. 606 $aPhilosophy$2bicssc 610 $aanalysis mode decomposition 610 $aanalysis-empirical mode decomposition 610 $adata-driven 610 $adeep learning 610 $aDNN 610 $adynamic analysis 610 $adynamic analysis of the structure 610 $adynamic fuzzy reliability analysis 610 $aempirical mode decomposition 610 $aextremum surface response method 610 $afault detection 610 $afull-scale static test 610 $afuzzy safety criterion 610 $ahealth monitoring 610 $alateral-river vibration 610 $alife cycle cost 610 $alithium-ion battery 610 $alow frequency tail fluctuation 610 $amachine learning 610 $amode mixing 610 $amultioperation condition 610 $aNAR neural network 610 $aneural networks 610 $anon-probabilistic reliability index 610 $aoffshore structures 610 $aoffshore wind turbines 610 $aoil and gas platforms 610 $aoperational modal analysis 610 $aoptimized deep belief networks 610 $aprobabilistic analyses of stochastic processes and frequency 610 $aprognostic and Health Management 610 $aprognostics 610 $aregeneration phenomenon 610 $areliability 610 $aremaining useful life 610 $arenewable energy 610 $aresidual useful life 610 $aretrofitting activities 610 $arotation of hydraulic generator 610 $asensitivity analysis 610 $asifting stop criterion 610 $asimilarity-based approach 610 $astochastic subspace identification 610 $astrain prediction 610 $astructural health monitoring 610 $asupervisory control and data acquisition system 610 $asupporting vector machine (SVM) 610 $atechno-economic assessments 610 $aturbine blisk 610 $aunderground powerhouse 610 $avertical axis wind turbine 610 $avibration test 610 $avibration transmission mechanism 610 $awave-structure interaction (WSI) 610 $awavelet decomposition 610 $aweighted regression 610 $awind and wave analysis 610 $awind turbine blade 610 $awind turbines 615 7$aPhilosophy 700 $aWang$b Dong$4auth$0596182 702 $aZhang$b Xiancheng$4auth 702 $aChen$b Gang$4auth 702 $aCorreia$b José A.F.O$4auth 702 $aQian$b Guian$4auth 702 $aZhu$b Shun-Peng$4auth 906 $aBOOK 912 $a9910372782603321 996 $aStructural Prognostics and Health Management in Power & Energy Systems$93020962 997 $aUNINA