| 000 | 03918nam a22005655i 4500 | ||
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| 001 | 978-981-10-6677-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220801221427.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180222s2018 si | s |||| 0|eng d | ||
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_a9789811066771 _9978-981-10-6677-1 |
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| 024 | 7 |
_a10.1007/978-981-10-6677-1 _2doi |
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| 050 | 4 | _aTH9701-9745 | |
| 072 | 7 |
_aTNKS _2bicssc |
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_aTEC032000 _2bisacsh |
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_aTNKS _2thema |
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_a621 _223 |
| 100 | 1 |
_aShang, Chao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _955964 |
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| 245 | 1 | 0 |
_aDynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research _h[electronic resource] / _cby Chao Shang. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2018. |
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| 300 |
_aXVIII, 143 p. 59 illus., 46 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5061 |
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| 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 | _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 |
_aSecurity systems. _931879 |
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| 650 | 0 |
_aManufactures. _931642 |
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| 650 | 0 |
_aControl engineering. _931970 |
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| 650 | 0 |
_aStatistics . _931616 |
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| 650 | 1 | 4 |
_aSecurity Science and Technology. _931884 |
| 650 | 2 | 4 |
_aMachines, Tools, Processes. _931645 |
| 650 | 2 | 4 |
_aControl and Systems Theory. _931972 |
| 650 | 2 | 4 |
_aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. _931790 |
| 710 | 2 |
_aSpringerLink (Online service) _955965 |
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| 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, _x2190-5061 _955966 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-10-6677-1 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
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