LEADER 04280nam 2201165z- 450 001 9910619469103321 005 20231214132934.0 010 $a3-0365-5174-3 035 $a(CKB)5670000000391583 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/93169 035 $a(EXLCZ)995670000000391583 100 $a20202210d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning-Based Machinery Fault Diagnostics 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (290 p.) 311 $a3-0365-5173-5 330 $aThis book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $aprocess monitoring 610 $adynamics 610 $avariable time lag 610 $adynamic autoregressive latent variables model 610 $asintering process 610 $ahammerstein output-error systems 610 $aauxiliary model 610 $amulti-innovation identification theory 610 $afractional-order calculus theory 610 $acanonical variate analysis 610 $adisturbance detection 610 $apower transmission system 610 $ak-nearest neighbor analysis 610 $astatistical local analysis 610 $aintelligent fault diagnosis 610 $astacked pruning sparse denoising autoencoder 610 $aconvolutional neural network 610 $aanti-noise 610 $aflywheel fault diagnosis 610 $abelief rule base 610 $afuzzy fault tree analysis 610 $aBayesian network 610 $aevidential reasoning 610 $aaluminum reduction process 610 $aalumina concentration 610 $asubspace identification 610 $adistributed predictive control 610 $aspatiotemporal feature fusion 610 $agated recurrent unit 610 $aattention mechanism 610 $afault diagnosis 610 $aevidential reasoning rule 610 $asystem modelling 610 $ainformation transformation 610 $aparameter optimization 610 $aevent-triggered control 610 $ainterval type-2 Takagi-Sugeno fuzzy model 610 $anonlinear networked systems 610 $afilter 610 $agearbox fault diagnosis 610 $aconvolution fusion 610 $astate identification 610 $aPSO 610 $awavelet mutation 610 $aLSSVM 610 $adata-driven 610 $aoperational optimization 610 $acase-based reasoning 610 $alocal outlier factor 610 $aabnormal case removal 610 $abearing fault detection 610 $adeep residual network 610 $adata augmentation 610 $acanonical correlation analysis 610 $ajust-in-time learning 610 $afault detection 610 $ahigh-speed trains 610 $aautonomous underwater vehicle 610 $athruster fault diagnostics 610 $afault tolerant control 610 $arobust optimization 610 $aocean currents 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aChen$b Hongtian$4edt$01063057 702 $aZhong$b Kai$4edt 702 $aRan$b Guangtao$4edt 702 $aCheng$b Chao$4edt 702 $aChen$b Hongtian$4oth 702 $aZhong$b Kai$4oth 702 $aRan$b Guangtao$4oth 702 $aCheng$b Chao$4oth 906 $aBOOK 912 $a9910619469103321 996 $aDeep Learning-Based Machinery Fault Diagnostics$93013732 997 $aUNINA