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基于IJB-PCA-ICA算法的故障检测
Alternative TitleFault detection based on IJB-PCA-ICA
刘舒锐1,2,3,4; 彭慧1,3,4; 李帅1,2,3,4; 周晓锋1,3,4
Department数字工厂研究室
Source Publication化工学报
ISSN0438-1157
2018
Volume69Issue:12Pages:5146-5154
Indexed ByCSCD
CSCD IDCSCD:6390165
Contribution Rank1
Funding Organization工信部智能制造综合标准化与新模式应用项目(Y6L8283A01)
Keyword主元分析 过程系统 过程控制 独立元分析 J-b检验
Abstract

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(JBtest)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并采用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并采用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。最后通过田纳西-伊斯曼(TE)仿真实验,有效验证了所提出方法的有效性。

Other Abstract

In 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 in this paper. 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.

Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22756
Collection数字工厂研究室
Corresponding Author李帅
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
3.中国科学院网络化控制系统重点实验室
4.中国科学院机器人与智能制造创新研究院
Recommended Citation
GB/T 7714
刘舒锐,彭慧,李帅,等. 基于IJB-PCA-ICA算法的故障检测[J]. 化工学报,2018,69(12):5146-5154.
APA 刘舒锐,彭慧,李帅,&周晓锋.(2018).基于IJB-PCA-ICA算法的故障检测.化工学报,69(12),5146-5154.
MLA 刘舒锐,et al."基于IJB-PCA-ICA算法的故障检测".化工学报 69.12(2018):5146-5154.
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