Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection
Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection
复制标题
多块统计局部核主成分分析算法及其在非线性过程故障检测中的应用
DOI：
10.1016/j.neucom.2019.09.075
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发表时间：
2020-02
期刊：
影响因子：
6
通讯作者：
Gu Xingsheng
中科院分区：
文献类型：
--
作者：
Zhou Bingqian;Gu Xingsheng
It is vital for fault detection technology to extract features of industrial process data effectively. Local kernel principal component analysis (LKPCA) has proved its good performance in preserving global and local structural characteristics. However, it ignored useful high-order statistics of data, so multi-block statistics local kernel principal component analysis (MSLKPCA) algorithm integrating statistics pattern analysis (SPA) into LKPCA is proposed. The correlation coefficient matrix is first calculated and K-means clustering is adopted to divide the original variables into several blocks. Then the weighted SPA, which gives different weights to different samples in each window according to their distributions, is adopted to build statistic spaces containing both low-order and high-order statistics. After that, LKCPA is performed in each statistic space to realize feature extraction. To reduce the noise effect amplified by SPA, PCA is adopted in the residual space to remove noise. Bayesian strategy is used to fuse the results of each block and two monitoring statisticsETandERare proposed to monitor the feature space and the residual space respectively. The Tennessee–Eastman (TE) process simulation shows the effectiveness and superiority of the proposed algorithm for process monitoring.
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影响因子：
7.3
作者：
I. Elaissi;Ines Jaffel;O. Taouali;H. Messaoud
通讯作者：
I. Elaissi;Ines Jaffel;O. Taouali;H. Messaoud
影响因子：
3.8
作者：
Hanyuan Zhang;Xuemin Tian;Xiaogang Deng;Lianfang Cai
通讯作者：
Hanyuan Zhang;Xuemin Tian;Xiaogang Deng;Lianfang Cai
基于局部结构分析的修正核主成分分析及其在非线性过程故障诊断中的应用
DOI：
10.1016/j.chemolab.2013.07.001
发表时间：
2013-08
影响因子：
3.9
作者：
Deng, Xiaogang;Tian, Xuemin;Chen, Sheng
通讯作者：
Chen, Sheng
影响因子：
4.2
作者：
E. Martin;A. Morris
通讯作者：
E. Martin;A. Morris
DOI：
10.1016/s1570-7946(05)80028-8
发表时间：
2005
期刊：
Computer-aided chemical engineering
影响因子：
--
作者：
David K. Wang;J. Romagnoli
通讯作者：
David K. Wang;J. Romagnoli