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Titlebook: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research; Chao Shang Book 2018 Springer Nature Singa

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發(fā)表于 2025-3-21 16:05:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
編輯Chao Shang
視頻videohttp://file.papertrans.cn/284/283657/283657.mp4
概述Nominated as an outstanding PhD thesis by Tsinghua University.Develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle.Proposes an e
叢書名稱Springer Theses
圖書封面Titlebook: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research; Chao Shang Book 2018 Springer Nature Singa
描述.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 industr
出版日期Book 2018
關(guān)鍵詞Industrial Process Control; Data-driven Methods; Process Data Analytics; Process Monitoring; Fault Diagn
版次1
doihttps://doi.org/10.1007/978-981-10-6677-1
isbn_softcover978-981-13-3889-2
isbn_ebook978-981-10-6677-1Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:45:28 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:41:26 | 只看該作者
Chao ShangNominated as an outstanding PhD thesis by Tsinghua University.Develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle.Proposes an e
地板
發(fā)表于 2025-3-22 06:50:03 | 只看該作者
5#
發(fā)表于 2025-3-22 11:11:48 | 只看該作者
Rohstoffwirtschaftliche EntwicklungshilfeThis chapter summarizes the whole work of this thesis, and some challenges on data-driven modeling methodologies and industrial applications are pointed out as the future work.
6#
發(fā)表于 2025-3-22 15:07:13 | 只看該作者
Conclusions and Recommendations,This chapter summarizes the whole work of this thesis, and some challenges on data-driven modeling methodologies and industrial applications are pointed out as the future work.
7#
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發(fā)表于 2025-3-23 00:11:09 | 只看該作者
Problemstellung und Zielsetzung,ondition is detected, an alarm is triggered based on classical monitoring methods. Consequently, they cannot distinguish real faults incurring dynamics anomalies from normal deviations in operating conditions. In this chapter, a new process monitoring strategy based on slow feature analysis (SFA) is
9#
發(fā)表于 2025-3-23 02:34:44 | 只看該作者
O. Frankl,K. Kaufmann,O. Schult?-Braunsdynamics described by SFA essentially is related to control performance. Then we propose a new data-driven control performance monitoring approach based on SFA. In addition, by employing the contribution plot techniques, we further develop a new control performance diagnosis method to locate potenti
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發(fā)表于 2025-3-23 08:05:48 | 只看該作者
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