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Mathematical Problems in Engineering
Volume 2013, Article ID 768018, 12 pages
Research Article

Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

1College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
2School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China

Received 25 March 2013; Revised 27 June 2013; Accepted 2 July 2013

Academic Editor: Jyh-Horng Chou

Copyright © 2013 Shu-zhi Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.