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001 978-981-10-6677-1
003 DE-He213
005 20211216140636.0
008 180222s2018 si | s |||| 0|eng d
020 _a9789811066771
040 _cAIKTC-KRRC
041 _aENG
072 7 _aTGPR
_2bicssc
072 7 _aTEC032000
_2bisacsh
072 7 _aTGPR
_2thema
082 0 4 _a658.56
_223
100 1 _aShang, Chao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
_h[electronic resource] /
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2018.
300 _aXVIII, 143 p. 59 illus., 46 illus. in color.
_bCard Paper
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
520 _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.
650 0 _aMechanical Engineering
_94626
653 _aQuality Control, Reliability, Safety and Risk.
653 _aManufacturing, Machines, Tools, Processes.
653 _aControl and Systems Theory.
653 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811066764
776 0 8 _iPrinted edition:
_z9789811066788
776 0 8 _iPrinted edition:
_z9789811338892
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttps://doi.org/10.1007/978-981-10-6677-1
_zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942 _cEBOOKS
_2ddc