Statistical and intelligent methods for default diagnosis and loacalization in a continuous tubular reactor

• Main Author: Liu, Haoran
• Language: ENG
• Published: INSA de Rouen 2009
• Subjects: [PHYS] Physics; Fault diagnosis; Ga; Alaga-rbf; Continuous tubular reactor
• Online Access: http://tel.archives-ouvertes.fr/tel-00560886; http://tel.archives-ouvertes.fr/docs/00/56/08/86/PDF/Thesis_of_LIU_DEC.8_2009.pdf

The aim is to study a continuous chemical process, and then analyze the hold process of the reactor and build the models which could be trained to realize the fault diagnosis and localization in the process. An experimental system has been built to be the research base. That includes experiment part and record system. To the diagnosis and localization methods, the work presented the methods with the data-based approach, mainly the Bayesian network and RBF network based on GAAPA (Genetic Algorithm with Auto-adapted of Partial Adjustment). The data collected from the experimental system are used to train and test the models.