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A Study for Chemical Industry Process Fault Diagnosis Based on Rough Set

Author: ZhuJiangHua
Tutor: PanFeng
School: Jiangnan University
Course: Control Theory and Control Engineering
Keywords: Fault Diagnosis Rough Set Knowledge Reduction Fuzzy Rough Sets Neural Network
CLC: TP277
Type: Master's thesis
Year: 2006
Downloads: 203
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Abstract


The production of chemical industry having a high degree of continuity , thus ensuring the stability and reliability of the production process becomes very important . With the large number of applications of modern computer monitoring system , chemical companies have accumulated a lot of the production process , the process and control variables data , these data contains a lot of production information . How to make full use of these data for chemical process fault diagnosis currently a hot research topic . Reduction from the knowledge representation system for continuous attributes using fuzzy rough set reduction and rough set and neural network combined three Tennessee - Eastman process ( TEP ) fault data , which established based on rough Set Theory multilayer neural network fault diagnosis system model . First in-depth study using rough set theory ( Rough Set RS ) data reduction method . In the analysis and study of the advantages and disadvantages of nuclear - based reduction method at the same time , the use of ant colony algorithm positive feedback and distributed computing features , a new rough set knowledge reduction based on ant colony algorithm , simulation the calculation results show that the method is fast validity . Secondly, the knowledge reduction for the rough set theory can not handle continuous attributes inherent defects , fuzzy rough set model , thus bypassing the process of discretization of continuous attributes , efficient use of continuous attribute fuzzy membership in the reduction process degree of information to enhance the utilization of the knowledge representation system the reduction achieved good effect . Again , while a comprehensive introduction to the combination of rough set and neural network Research , based on rough set theory and neural network , rough neural network structure model of a strong coupling . Rule extraction using rough set theory , the rule results into neural network to construct a strong coupling of multi-layer fuzzy rough neural network . Finally, the characteristics of the TEP process , the TEP process fault diagnosis based on fuzzy rough set theory multilayer artificial neural network fault diagnosis system , give full play to their respective advantages of rough set and neural network to simplify the structure of the neural network to improve speed of fault diagnosis , fault diagnosis .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Monitoring, alarm,fault diagnosis system
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