LEADER 03967nam 2201081z- 450 001 9910557491503321 005 20231214132842.0 035 $a(CKB)5400000000042916 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76899 035 $a(EXLCZ)995400000000042916 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Process Monitoring for Industry 4.0 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (288 p.) 311 $a3-0365-2073-2 311 $a3-0365-2074-0 330 $aThis book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and ?extreme data? conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes. 606 $aTechnology: general issues$2bicssc 610 $aspatial-temporal data 610 $apasting process 610 $aprocess image 610 $aconvolutional neural network 610 $aIndustry 4.0 610 $aauto machine learning 610 $afailure mode effects analysis 610 $arisk priority number 610 $arolling bearing 610 $acondition monitoring 610 $aclassification 610 $aOPTICS 610 $astatistical process control 610 $acontrol chart pattern 610 $adisruptions 610 $adisruption management 610 $afault diagnosis 610 $aconstruction industry 610 $aplaster production 610 $aneural networks 610 $adecision support systems 610 $aexpert systems 610 $afailure mode and effects analysis (FMEA) 610 $adiscriminant analysis 610 $anon-intrusive load monitoring 610 $aload identification 610 $amembrane 610 $adata reconciliation 610 $areal-time 610 $aonline 610 $amonitoring 610 $aSix Sigma 610 $amultivariate data analysis 610 $alatent variables models 610 $aPCA 610 $aPLS 610 $ahigh-dimensional data 610 $astatistical process monitoring 610 $aartificial generation of variability 610 $adata augmentation 610 $aquality prediction 610 $acontinuous casting 610 $amultiscale 610 $atime series classification 610 $aimbalanced data 610 $acombustion 610 $aoptical sensors 610 $aspectroscopy measurements 610 $asignal detection 610 $adigital processing 610 $aprincipal component analysis 610 $acurve resolution 610 $adata mining 610 $asemiconductor manufacturing 610 $aquality control 610 $ayield improvement 610 $afault detection 610 $aprocess control 610 $amulti-phase residual recursive model 610 $amulti-mode model 610 $aprocess monitoring 615 7$aTechnology: general issues 700 $aReis$b Marco S$4edt$01325356 702 $aGao$b Furong$4edt 702 $aReis$b Marco S$4oth 702 $aGao$b Furong$4oth 906 $aBOOK 912 $a9910557491503321 996 $aAdvanced Process Monitoring for Industry 4.0$93036793 997 $aUNINA