书目名称 | Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research | 编辑 | Chao Shang | 视频video | | 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 | 丛书名称 | Springer Theses | 图书封面 |  | 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 | 关键词 | Industrial Process Control; Data-driven Methods; Process Data Analytics; Process Monitoring; Fault Diagn | 版次 | 1 | doi | https://doi.org/10.1007/978-981-10-6677-1 | isbn_softcover | 978-981-13-3889-2 | isbn_ebook | 978-981-10-6677-1Series ISSN 2190-5053 Series E-ISSN 2190-5061 | issn_series | 2190-5053 | copyright | Springer Nature Singapore Pte Ltd. 2018 |
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