[1]翟 超,熊伟丽.一种分层信息提取的多块主元分析故障监测方法[J].南京理工大学学报(自然科学版),2020,44(04):471-480.[doi:10.14177/j.cnki.32-1397n.2020.44.04.012]
 Zhai Chao,Xiong Weili.Multi-block fault monitoring based on PCA method withhierarchical information extraction[J].Journal of Nanjing University of Science and Technology,2020,44(04):471-480.[doi:10.14177/j.cnki.32-1397n.2020.44.04.012]
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一种分层信息提取的多块主元分析故障监测方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年04期
页码:
471-480
栏目:
出版日期:
2020-08-30

文章信息/Info

Title:
Multi-block fault monitoring based on PCA method withhierarchical information extraction
文章编号:
1005-9830(2020)04-0471-10
作者:
翟 超熊伟丽
江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
Author(s):
Zhai ChaoXiong Weili
School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China
关键词:
分层信息提取 互信息 主元分析 故障监测 多块建模
Keywords:
hierarchical information extraction mutual information principal component analysis fault monitoring multi-block modelling
分类号:
TP277
DOI:
10.14177/j.cnki.32-1397n.2020.44.04.012
摘要:
针对传统多块故障监测方法建模时,一般仅考虑到过程局部信息的问题,提出一种分层信息提取的多块主元分析(Principal component analysis,PCA)故障监测方法。第一层考虑过程变量之间的互信息,对过程变量进行分块,提取过程局部信息; 第二层对每个变量块进一步提取累计误差和二阶差分等特征信息,结合观测值信息共同将每个变量块扩展为3个信息子块; 对每个信息子块采用PCA方法进行监测,并基于贝叶斯方法融合所有信息子块的监测结果。所提方法的有效性和性能在田纳西-伊斯曼(Tennessee-Eastman,TE)过程监控中进行了验证和分析。
Abstract:
The traditional multi-block fault monitoring methods only use local information of the process,and other valid information of the data set is ignored. In view of this problem,a multi-block principal component analysis(PCA)fault monitoring method with hierarchical information extraction is proposed. Firstly,this paper calculates the mutual information between the process variables and blocks the process variables to extract the process local information. Second,this paper further digs the feature information of the process data,extracts the cumulative error and second-order difference information for each variable block,and with the observed values information expands each variable block into three information sub-blocks. Each sub-block is monitored by the PCA method,and the monitoring results of all the sub-blocks are integrated based on the Bayesian method. The validity and performance of the proposed method are verified and analyzed in Tennessee-Eastman(TE)process monitoring.

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备注/Memo

备注/Memo:
收稿日期:2019-12-11 修回日期:2020-05-12
基金项目:国家自然科学基金(61773182); 国家重点研发计划子课题(2018YFC1603705-03); 江苏高校“青蓝工程”资助项目
作者简介:翟超(1996-),男,硕士生,主要研究方向:工业过程监控,E-mail:15061884164@163.com; 通讯作者:熊伟丽(1978-),女,博士,教授,主要研究方向:复杂工业过程建模与优化,智能软测量技术,E-mail:greenpre@163.com。
引文格式:翟超,熊伟丽. 一种分层信息提取的多块主元分析故障监测方法[J]. 南京理工大学学报,2020,44(4):471-480.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-08-30