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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research (Springer Theses)

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en Limba Engleză Carte Paperback – 19 Mar 2019
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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Specificații

ISBN-13: 9789811338892
ISBN-10: 9811338892
Dimensiuni: 155 x 235 mm
Greutate: 0.24 kg
Ediția: Softcover reprint of the original 1st ed. 2018
Editura: Springer
Colecția Springer
Seria Springer Theses

Locul publicării: Singapore, Singapore

Cuprins

Introduction.- Concurrent monitoring of steady state and process dynamics with SFA.- Online monitoring and diagnosis of control performance with SFA and contribution plots.- Recursive SFA algorithm and adaptive monitoring system design.- Probabilistic SFR model and its applications in dynamic quality prediction.- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction.- Nonlinear and dynamic soft sensing model based on Bayesian framework.- Summary and open problems.


Textul de pe ultima copertă

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.

Caracteristici

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