LEADER 02974nam 22006373 450 001 9910520099003321 005 20231110213129.0 010 $a981-16-8044-2 035 $a(CKB)5340000000068900 035 $a(MiAaPQ)EBC6840160 035 $a(Au-PeEL)EBL6840160 035 $a(OCoLC)1292353116 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77320 035 $a(PPN)262175452 035 $a(EXLCZ)995340000000068900 100 $a20220207d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-Driven Fault Detection and Reasoning for Industrial Monitoring 210 $cSpringer Nature$d2022 210 1$aSingapore :$cSpringer Singapore Pte. Limited,$d2022. 210 4$dİ2022. 215 $a1 online resource (277 pages) 225 1 $aIntelligent Control and Learning Systems ;$vv.3 311 $a981-16-8043-4 330 $aThis open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book. 410 0$aIntelligent Control and Learning Systems 606 $aRobotics$2bicssc 606 $aArtificial intelligence$2bicssc 610 $aMultivariate causality analysis 610 $aProcess monitoring 610 $aManifold learning 610 $aFault diagnosis 610 $aData modeling 610 $aFault classification 610 $aFault reasoning 610 $aCausal network 610 $aProbabilistic graphical model 610 $aData-driven methods 610 $aIndustrial monitoring 610 $aOpen Access 615 7$aRobotics 615 7$aArtificial intelligence 700 $aWang$b Jing$f1974 April 21-$01380923 701 $aZhou$b Jinglin$01076407 701 $aChen$b Xiaolu$01076408 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910520099003321 996 $aData-Driven Fault Detection and Reasoning for Industrial Monitoring$93423101 997 $aUNINA