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Sinopsis
This 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.
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De la contraportada
This 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.
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research (eng)
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Dynamic Modeling of Complex Industrial Processes : Data-driven Methods and Application Research
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
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Springer Nature Singapore Mrz 2018, 2018
ISBN 10: 9811066760
ISBN 13: 9789811066764
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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. 164 pp. Englisch. Nº de ref. del artículo: 9789811066764
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Dynamic Modeling of Complex Industrial Processes : Data-driven Methods and Application Research
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This 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 . Nº de ref. del artículo: 160354397
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research (Springer Theses)
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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. Nº de ref. del artículo: 9789811066764
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research (Springer Theses)
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
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Springer, Springer Mär 2018, 2018
ISBN 10: 9811066760
ISBN 13: 9789811066764
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nominated as an outstanding PhD thesis by Tsinghua UniversityDevelops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principleProposes an effective process monitoring strategy to eliminate false alarms in industrial productionPresents a holistic framework for adaptive process monitoring system designOffers dynamic quality prediction models with improved data utilization and accuracy for product quality controlSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 164 pp. Englisch. Nº de ref. del artículo: 9789811066764
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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
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Hardcover. Condición: Brand New. 161 pages. 9.25x6.10x0.60 inches. In Stock. Nº de ref. del artículo: x-9811066760
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