基于改进KFDA和RW ν-SVM的化工生产系统故障快速诊断  

A Fast Method for Diagnosing Fault in Chemical Production System Based on Improved KFDA and RW ν-SVM

作　　者：王斌[1] 施祖建[1] 匡蕾[1] 

机构地区：[1]江苏省安全生产科学研究院化工安全与信息研究所,江苏南京210009

出　　处：《中国安全科学学报》2013年第8期84-89,共6页China Safety Science Journal

基　　金：江苏省科技基础设施建设计划项目(BM2012067)

摘　　要：为提高复杂化工生产系统在线故障诊断的效率和准确率,将改进核费舍尔主元分析法(KFDA)和鲁棒损失小波ν-支持向量机法(RWν-SVM)结合。首先,利用近邻边界法对KFDA进行监督降维,快速辨识和提取化工过程影响因素的核主元。然后,将核主元作为诊断和分类RWν-SVM的输入参数,并优化回归决策函数表达式,使诊断过程更加快速,分类更加准确。最后,设计一个基于改进KFDA和RWν-SVM算法,并以经典的田纳西-伊士曼化工过程(TEP)为实例进行计算。结果表明:用改进的算法,能快速诊断和分类化工生产系统中的故障,且在计算效率和正确率方面均优于普通方法,故障诊断结果能够反映化工过程的实际情况。To improve the efficiency and accuracy of on-line fault diagnosis of complex chemical process production system, an improved fast fault diagnosis method combined with improved KFDA and robust wavelet u-SVM was worked out. Firstly, the neighbor boundary method was used for dimensionality reduction of KFDA, which helped to realize the rapid identification and extraction of kernel principal component of sample. Secondly, the kernel principal components acquired by KFDA were used as input parameters for diagnosis and classification of RWu-SVM, and the regression decision function was optimized, which made the diagnosis process rapider and classification more accurate. Finally, an algorithm based on improved KFDA and RWu-SVM was designed for fault diagnosis and classification. The classical TEP （Tennessee Eastman Process） chemical process was employed as background examples. The calculation results show that improved method can do not only fast fault diagnosis and classification for a chemical pro- duction system, but also, compared with common fault diagnosis method, has better fault diagnosis speed and accuracy, which can reflect the actual situation of chemical production system.

关 键 词：化工过程 快速故障诊断 核费舍尔主元分析法(KFDA) 支持向量机(SVM) 分类算法 

分 类 号：X937[环境科学与工程—安全科学]