文章基本信息

标题：Rough Set-Probabilistic Neural Networks Fault Diagnosis Method of Polymerization Kettle Equipment Based on Shuffled Frog Leaping Algorithm
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作者：Jie-Sheng Wang ; Jiang-Di Song
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2015
卷号：6
期号：1
页码：49-68
DOI：10.3390/info6010049
语种：English
出版社：MDPI Publishing
摘要：In order to realize the fault diagnosis of the polyvinyl chloride (PVC) polymerization kettle reactor, a rough set (RS)–probabilistic neural networks (PNN) fault diagnosis strategy is proposed. Firstly, through analysing the technique of the PVC polymerization reactor, the mapping between the polymerization process data and the fault modes is established. Then, the rough set theory is used to tackle the input vector of PNN so as to reduce the network dimensionality and improve the training speed of PNN. Shuffled frog leaping algorithm (SFLA) is adopted to optimize the smoothing factor of PNN. The fault pattern classification of polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the fault diagnosis simulation experiments are conducted by combining with the industrial on-site historical datum of polymerization kettle, and the results show that the RS–PNN fault diagnosis strategy is effective.
关键词：polymerization kettle equipment; fault diagnosis; rough set; probabilistic neural networks; shuffled frog leaping algorithm polymerization kettle equipment ; fault diagnosis ; rough set ; probabilistic neural networks ; shuffled frog leaping algorithm