Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
Language: English
Published by Springer, Springer Jan 2019, 2019
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
AbeBooks seller since January 23, 2017
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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.
Seller Inventory # 9789811338892
- Title
- Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
- Author
- Chao Shang
- Publisher
- Springer, Springer Jan 2019
- Publication year
- 2019
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9811338892
- ISBN 13
- 9789811338892
- Item weight
- 260 grams
- Dimensions
- 235x155x10 mm
- Series
- Book 413 of 797: Springer Theses
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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