Novel fault monitoring strategy for chemical process based on KECA

doi:10.11949/j.issn.0438-1157.20151899

CIESC Journal ›› 2016, Vol. 67 ›› Issue (3): 1063-1069.DOI: 10.11949/j.issn.0438-1157.20151899

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Novel fault monitoring strategy for chemical process based on KECA

QI Yongsheng^1,3, ZHANG Haili¹, GAO Xuejin^2,3, WANG Pu^2,3

1. Institute of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia, China;
2. School of Electric and Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
3. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China

Received:2015-12-14 Revised:2015-12-16 Online:2016-01-12 Published:2016-03-05
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (61174109, 61364009) and the Natural Science Foundation of Inner Mongolia (2015MS0615).

基于KECA的化工过程故障监测新方法

齐咏生^1,3, 张海利¹, 高学金^2,3, 王普^2,3

1. 内蒙古工业大学电力学院, 内蒙古呼和浩特 010051;
2. 北京工业大学电子信息与控制工程学院, 北京 100124;
3. 教育部数字工程研究中心, 北京 100124

通讯作者: 齐咏生
基金资助:
国家自然科学基金项目(61174109,61364009);内蒙古自治区自然科学基金项目(2015MS0615)。

Abstract

Abstract:

A chemical process fault monitoring algorithms based on kernel entropy component analysis (KECA) is presented for the complexity and nonlinear of industrial chemical process data. The number of principal components selected by the KECA algorism is much less than the KPCA algorism, which can effectively reduce computational complexity. This is achieved by selections onto eigenvalue and eigenvector based on the value of Renyi entropy. Research shows that KECA reveals angular structure relating to the Renyi entropy of the input space data set. A new statistic—Cauchy-Schwarz divergence measure, namely the cosine value between vectors in kernel space, is proposed, which describes the similarity between different PDFs (probability density functions). It is shown that KECA has great advantages in detection latency and fault detection rate in comparing to KPCA by applying them to TE (Tennessee Eastman) process respectively.

Key words: safety, process control, principal component analysis, fault monitoring, KECA, CS statistic

摘要：

针对化工过程数据复杂、非线性的特点,提出一种基于核熵成分分析(KECA)的化工过程故障监测算法。首先,KECA算法按照Renyi熵值的大小选取特征值及特征向量,相比传统的KPCA监测算法,其保留主元个数更少,可以有效减少运算量。同时,仿真研究表明KECA算法选取的主元具有角度结构特性,据此,提出一种新的统计量——CS(Cauchy-Schwarz)统计量,其对应到核特征空间中即为向量间的角度余弦值,可以较好表述不同概率密度分布之间的相似度。最后,将KECA和KPCA算法分别应用于TE(Tennessee Eastman)过程,结果表明KECA在故障检测延迟与检出率相比KPCA都有很大的优势。

关键词: 安全, 过程控制, 主元分析, 故障监测, KECA, CS统计量

CLC Number:

TP277

QI Yongsheng, ZHANG Haili, GAO Xuejin, WANG Pu. Novel fault monitoring strategy for chemical process based on KECA[J]. CIESC Journal, 2016, 67(3): 1063-1069.

齐咏生, 张海利, 高学金, 王普. 基于KECA的化工过程故障监测新方法[J]. 化工学报, 2016, 67(3): 1063-1069.

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Novel fault monitoring strategy for chemical process based on KECA

基于KECA的化工过程故障监测新方法

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