LEADER 00973nam 2200253la 450 001 9910482028103321 005 20221108102059.0 035 $a(UK-CbPIL)2090326951 035 $a(CKB)5500000000087590 035 $a(EXLCZ)995500000000087590 100 $a20210618d1637 uy | 101 0 $adut 135 $aurcn||||a|bb| 200 10$a's Werelts begin, midden, eynde besloten in den trov-ringh, met den proef-steen van den selven. Door I. Cats$b[electronic resource] 210 $aDordrecht $cHendrick van Esch$d1637 215 $aOnline resource ([47], 772, 136 p, 4°) 300 $aReproduction of original in Koninklijke Bibliotheek, Nationale bibliotheek van Nederland. 700 $aCats$b Jacob$f1577-1660.$0677702 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910482028103321 996 $aS Werelts begin, midden, eynde besloten in den trov-ringh, met den proef-steen van den selven. Door I. Cats$92281239 997 $aUNINA LEADER 03673nam 2200685 a 450 001 9910437928003321 005 20200520144314.0 010 $a9781283910620 010 $a1283910624 010 $a9781447145134 010 $a1447145135 024 7 $a10.1007/978-1-4471-4513-4 035 $a(CKB)2670000000308612 035 $a(EBL)1081725 035 $a(OCoLC)822977298 035 $a(SSID)ssj0000811449 035 $a(PQKBManifestationID)11510758 035 $a(PQKBTitleCode)TC0000811449 035 $a(PQKBWorkID)10850208 035 $a(PQKB)11741325 035 $a(DE-He213)978-1-4471-4513-4 035 $a(MiAaPQ)EBC1081725 035 $a(PPN)16829365X 035 $a(EXLCZ)992670000000308612 100 $a20120820d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultivariate statistical process control $eprocess monitoring methods and applications /$fZhiqiang Ge, Zhihuan Song 205 $a1st ed. 2013. 210 $aLondon ;$aNew York $cSpringer$dc2013 215 $a1 online resource (203 p.) 225 0$aAdvances in industrial control,$x1430-9491 300 $aDescription based upon print version of record. 311 08$a9781447159896 311 08$a1447159896 311 08$a9781447145127 311 08$a1447145127 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- An Overview of Conventional MSPC Methods -- Non-Gaussian Process Monitoring -- Fault Reconstruction and Identification -- Nonlinear Process Monitoring: Part I -- Nonlinear Process Monitoring: Part 2 -- Time-varying Process Monitoring -- Multimode Process Monitoring: Part 1 -- Multimode Process Monitoring: Part 2 -- Dynamic Process Monitoring -- Probabilistic Process Monitoring -- Plant-wide Process Monitoring: Multiblock Method -- Reference -- Index. 330 $a  Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas.   Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. 410 0$aAdvances in Industrial Control,$x1430-9491 606 $aProcess control 606 $aMultivariate analysis 615 0$aProcess control. 615 0$aMultivariate analysis. 676 $a658.5/62 676 $a658.562 676 $a658.56201519535 700 $aGe$b Zhiqiang$01064380 701 $aSong$b Zhihuan$01752837 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437928003321 996 $aMultivariate statistical process control$94188323 997 $aUNINA