Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network
Joint Authors
Gao, Shu-zhi
Zhao, Na
Wang, Jie-sheng
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-30
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Polyvinyl chloride (PVC) polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay.
Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks.
The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively.
Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set.
Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method.
The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.
American Psychological Association (APA)
Gao, Shu-zhi& Wang, Jie-sheng& Zhao, Na. 2013. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1032154
Modern Language Association (MLA)
Gao, Shu-zhi…[et al.]. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network. Mathematical Problems in Engineering No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-1032154
American Medical Association (AMA)
Gao, Shu-zhi& Wang, Jie-sheng& Zhao, Na. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1032154
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032154