基于IJB-PCA-ICA算法的故障检测

doi:10.11949/j.issn.0438-1157.20180643

化工学报 ›› 2018, Vol. 69 ›› Issue (12): 5146-5154.DOI: 10.11949/j.issn.0438-1157.20180643

基于IJB-PCA-ICA算法的故障检测

刘舒锐^1,2,3,4, 彭慧^1,3,4, 李帅^1,2,3,4, 周晓锋^1,3,4

1. 中国科学院沈阳自动化研究所, 辽宁沈阳 110016;
2. 中国科学院大学, 北京 100049;
3. 中国科学院网络化控制系统重点实验室, 辽宁沈阳 110016;
4. 中国科学院机器人与智能制造创新研究院, 辽宁沈阳 110016

收稿日期:2018-06-11 修回日期:2018-09-10 出版日期:2018-12-05 发布日期:2018-12-05
通讯作者: 李帅
基金资助:
工信部智能制造综合标准化与新模式应用项目（Y6L8283A01）。

Fault detection based on IJB-PCA-ICA

LIU Shurui^1,2,3,4, PENG Hui^1,3,4, LI Shuai^1,2,3,4, ZHOU Xiaofeng^1,3,4

1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China;
4. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China

Received:2018-06-11 Revised:2018-09-10 Online:2018-12-05 Published:2018-12-05
Supported by:
supported by the Intelligent Manufacturing Comprehensive Standardization and New Pattern Application Project of MIIT (Y6L8283A01).

摘要/Abstract

摘要：

针对现代工业过程数据的高维性和分布复杂性等问题，提出了一种基于IJB-PCA-ICA（improved Jarque-Bera-principal component analysis-independent component analysis）的故障检测方法。首先采用改进的Jarque-Bera检测方法（J-B test）对原始数据划分高斯与非高斯核心部分，并对其中的高斯性与非高斯性均不明显的变量划分半高斯部分。将半高斯部分通过高斯分布置信概率加权与高斯核心部分和非高斯核心部分分别建立高斯子空间和分高斯子空间，然后对高斯子空间进行相关性划分后采用PCA方法得到高斯子空间的统计量；对非高斯子空间进行主元投影划分后采用ICA方法得到非高斯子空间的统计量，接着通过贝叶斯推断得到的联合统计量进行故障检测。最后通过Tenessee Eastman（TE）仿真实验，有效验证了所提出方法的有效性。

关键词: 主元分析, 过程系统, 过程控制, 独立元分析, J-B检验

Abstract:

In the view of the high dimensionality and the distribution complexity of modern industrial data, a fault detection method based on IJB-PCA-ICA (improved Jarque-Bera-principal component analysis-independent component analysis) is proposed. Through the method of Jarque-Bera test (J-B test), the original data are divided into Gaussian part, non-Gaussian part and semi-Gaussian part. The semi-Gaussian part divided from those variables with not obvious Gaussianity or non-Gaussianity is weighted to participate into Gaussian subspace and non-Gaussian subspace by the Gaussian confidence probability. After the partition by the correlation and principal component projection, the statistics of the Gaussian and non-Gaussian subspaces are obtained by PCA and ICA, respectively. Then the Bayesian inference is applied to obtain the comprehensive statistics for fault detection.

Key words: principal component analysis, process system, process control, independent component analysis, J-B test

中图分类号:

TP277

刘舒锐, 彭慧, 李帅, 周晓锋. 基于IJB-PCA-ICA算法的故障检测[J]. 化工学报, 2018, 69(12): 5146-5154.

LIU Shurui, PENG Hui, LI Shuai, ZHOU Xiaofeng. Fault detection based on IJB-PCA-ICA[J]. CIESC Journal, 2018, 69(12): 5146-5154.

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基于IJB-PCA-ICA算法的故障检测

Fault detection based on IJB-PCA-ICA

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