LEADER 04573nam 22006855 450 001 9910299942203321 005 20200706103542.0 010 $a981-10-6677-9 024 7 $a10.1007/978-981-10-6677-1 035 $a(CKB)4100000002485527 035 $a(MiAaPQ)EBC5309345 035 $a(DE-He213)978-981-10-6677-1 035 $a(PPN)224638602 035 $a(EXLCZ)994100000002485527 100 $a20180222d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aDynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research /$fby Chao Shang 205 $a1st ed. 2018. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018. 215 $a1 online resource (154 pages) $cillustrations, tables 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 300 $a"Doctoral thesis accepted by Tsinghua University, Beijing, China." 311 $a981-10-6676-0 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Concurrent monitoring of steady state and process dynamics with SFA -- Online monitoring and diagnosis of control performance with SFA and contribution plots -- Recursive SFA algorithm and adaptive monitoring system design -- Probabilistic SFR model and its applications in dynamic quality prediction -- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction -- Nonlinear and dynamic soft sensing model based on Bayesian framework -- Summary and open problems. 330 $aThis thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 606 $aQuality control 606 $aReliability 606 $aIndustrial safety 606 $aManufactures 606 $aAutomatic control 606 $aStatistics 606 $aQuality Control, Reliability, Safety and Risk$3https://scigraph.springernature.com/ontologies/product-market-codes/T22032 606 $aManufacturing, Machines, Tools, Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/T22050 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 615 0$aQuality control. 615 0$aReliability. 615 0$aIndustrial safety. 615 0$aManufactures. 615 0$aAutomatic control. 615 0$aStatistics. 615 14$aQuality Control, Reliability, Safety and Risk. 615 24$aManufacturing, Machines, Tools, Processes. 615 24$aControl and Systems Theory. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a670.42015118 700 $aShang$b Chao$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063356 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299942203321 996 $aDynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research$92531934 997 $aUNINA