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008			180222s2018 si | s |||| 0|eng d
020			_a9789811066771
024	7		_a10.1007/978-981-10-6677-1 _2doi
040			_bspa _dES-MaUEC
050		4	_aTS183
100	1		_aShang, Chao. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/96607803/
245	1	0	_aDynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research _cby Chao Shang.
264		1	_aSingapore _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XVIII, 143 páginas 59 ilustraciones, 46 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053
490	0		_aEngineering (Springer-11647)
505	0		_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.
520	3		_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.
988			_aEBSPRINGER_2018
650		7	_aProcesos de fabricación _2embne _9163432
776	0	8	_iEdición impresa: _z9789811066764
776	0	8	_iEdición impresa: _z9789811066788
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-10-6677-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _zSI _eIG