LEADER 03723nam 22006015 450 001 9910437928003321 005 20200701042902.0 010 $a1-283-91062-4 010 $a1-4471-4513-5 024 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 $a20121205d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultivariate Statistical Process Control$b[electronic resource] $eProcess Monitoring Methods and Applications /$fby Zhiqiang Ge, Zhihuan Song 205 $a1st ed. 2013. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2013. 215 $a1 online resource (203 p.) 225 1 $aAdvances in Industrial Control,$x1430-9491 300 $aDescription based upon print version of record. 311 $a1-4471-5989-6 311 $a1-4471-4512-7 320 $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 $aControl engineering 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 615 0$aControl engineering. 615 14$aControl and Systems Theory. 676 $a658.5/62 676 $a658.562 676 $a658.56201519535 700 $aGe$b Zhiqiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064380 702 $aSong$b Zhihuan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910437928003321 996 $aMultivariate Statistical Process Control$92537746 997 $aUNINA