Overview
- 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 effective process monitoring strategy to eliminate false alarms in industrial production
- Presents a holistic framework for adaptive process monitoring system design
- Offers dynamic quality prediction models with improved data utilization and accuracy for product quality control
Part of the book series: Springer Theses (Springer Theses)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
Keywords
About this book
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.
Â
Authors and Affiliations
Bibliographic Information
Book Title: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
Authors: Chao Shang
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-981-10-6677-1
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-10-6676-4Published: 05 March 2018
Softcover ISBN: 978-981-13-3889-2Published: 30 January 2019
eBook ISBN: 978-981-10-6677-1Published: 22 February 2018
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVIII, 143
Number of Illustrations: 13 b/w illustrations, 46 illustrations in colour
Topics: Quality Control, Reliability, Safety and Risk, Manufacturing, Machines, Tools, Processes, Control and Systems Theory, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences