Novel fault monitoring strategy for chemical process based on KECA

QI Yongsheng; ZHANG Haili; GAO Xuejin; WANG Pu

doi:10.11949/j.issn.0438-1157.20151899

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Novel fault monitoring strategy for chemical process based on KECA

更新时间：2025-12-10

- Novel fault monitoring strategy for chemical process based on KECA
- CIESC Journal Issue 3, Pages: 1063-1069(2016)
- 作者机构：
  
  1. 内蒙古工业大学电力学院,内蒙古,呼和浩特,010051
  2. 教育部数字工程研究中心,北京,100124
  3. 北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151899
  CLC： P277
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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QI Yongsheng, ZHANG Haili, GAO Xuejin, et al. Novel fault monitoring strategy for chemical process based on KECA[J]. CIESC Journal, 2016, (3): 1063-1069.
DOI：

QI Yongsheng, ZHANG Haili, GAO Xuejin, et al. Novel fault monitoring strategy for chemical process based on KECA[J]. CIESC Journal, 2016, (3): 1063-1069. DOI： 10.11949/j.issn.0438-1157.20151899.

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。

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.

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