Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

• Κύριος συγγραφέας: Shang, Chao (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Springer Theses, Recognizing Outstanding Ph.D. Research,
• Θέματα: Quality control.; Reliability.; Industrial safety.; Manufactures.; Control engineering.; Statistics .; Quality Control, Reliability, Safety and Risk.; Manufacturing, Machines, Tools, Processes.; Control and Systems Theory.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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...

Πίνακας περιεχομένων:

• 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.