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An improved KPCA algorithm of chemical process fault diagnosis based on RVM | |
Zhao, Xiaoqiang; Xue, Yongfei; Yang, Wu | |
2013-10-18 | |
会议名称 | 32nd Chinese Control Conference, CCC 2013 |
会议录名称 | Chinese Control Conference, CCC |
页码 | 6083-6087 |
会议日期 | July 26, 2013 - July 28, 2013 |
会议地点 | Xi'an, China |
出版者 | IEEE Computer Society |
摘要 | KPCA-SVM algorithm is a combination of kernel principal component analysis (KPCA) and support vector machine (SVM). It could increase the diagnosis time and decrease the diagnosis efficiency, because more relevant vectors are needed when it is used to monitor the on-line complex chemical process. According to this problem, another combined algorithm which is composed of kernel principal component analysis and relevance vector machine (RVM) is proposed in this paper. Firstly, KPCA-RVM algorithm uses KPCA to structure T2 statistics and SPE statistics in the feature space to detect fault, and then it takes the non-linear principal component score vector of samples as the input of relevance vector machine to identify the fault modes. KPCA-RVM algorithm is applied to Tennessee Eastman (TE) chemical process and many kinds of fault mode simulation results show that this algorithm not only can obtain higher fault diagnosis accuracy than KPCA-SVM, but also can raise the speed of fault diagnosis obviously owing to the less necessary relevant vectors. © 2013 TCCT, CAA. |
关键词 | Failure analysis Principal component analysis Process control Support vector machines Vector spaces Vectors Combined algorithms Fault identifications Kernel principal component analyses (KPCA) KPCA-RVM KPCA-SVM Relevance Vector Machine TE process Tennessee Eastman |
收录类别 | EI |
语种 | 中文 |
EI入藏号 | 20135217123006 |
EI主题词 | Fault detection |
ISSN | 19341768 |
来源库 | Compendex |
分类代码 | 723 Computer Software, Data Handling and Applications - 921 Mathematics - 921.1 Algebra - 922.2 Mathematical Statistics |
文献类型 | 会议论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/117519 |
专题 | 电气工程与信息工程学院 |
作者单位 | College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China |
第一作者单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Zhao, Xiaoqiang,Xue, Yongfei,Yang, Wu. An improved KPCA algorithm of chemical process fault diagnosis based on RVM[C]:IEEE Computer Society,2013:6083-6087. |
条目包含的文件 | 条目无相关文件。 |
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