基于分布式ICA-PCA模型的工业过程故障监测

doi:10.11949/j.issn.0438-1157.20150546

化工学报 ›› 2015, Vol. 66 ›› Issue (11): 4546-4554.DOI: 10.11949/j.issn.0438-1157.20150546

基于分布式ICA-PCA模型的工业过程故障监测

衷路生, 何东, 龚锦红, 张永贤

华东交通大学电气学院, 江西南昌 330013

收稿日期:2015-05-04 修回日期:2015-08-06 出版日期:2015-11-05 发布日期:2015-11-05
通讯作者: 衷路生
基金资助:
国家自然科学基金项目(61263010,60904049);江西省自然科学基金项目(20114BAB211014);江西省教育厅项目(GJJ14399)。

Fault monitoring of industrial process based on distributed ICA-PCA model

ZHONG Lusheng, HE Dong, GONG Jinhong, ZHANG Yongxian

College of Electrical Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Received:2015-05-04 Revised:2015-08-06 Online:2015-11-05 Published:2015-11-05
Supported by:
supported by the National Natural Science Foundation of China (61263010, 60904049), the Natural Science Foundation of Jiangxi Province (20114BAB211014) and the Project of Education Department of Jiangxi Province (GJJ14399).

摘要/Abstract

摘要：

提出基于分布式ICA-PCA( independent component analysis-principal component analysis)模型的工业过程故障监测方法,适合于复杂工业过程难以自动划分子块及过程数据存在非高斯信息的情况。首先,对过程数据进行PCA分解,并在PCA主成分不同的方向上构建不同的子块,把原始特征空间自动划分为不同子空间。然后,对各个子块采用ICA-PCA两步信息提取的策略,提取出高斯信息和非高斯信息,并构建新的统计量和统计限。最后,通过Tennessee Eastman(TE)过程的仿真实验,验证所提出故障监测模型的有效性和可行性。

关键词: 复杂工业过程, 自动划分子块, 非高斯, ICA-PCA, 故障监测

Abstract:

A fault monitoring method based on distributed independent component analysis-principal component analysis (ICA-PCA) model is proposed, which is suitable for complex industrial process that cannot be divided into several sub-blocks through an automatic way and has non-Gaussian information. Firstly, an initial PCA decomposition is carried out upon the variables of the whole process. By constructing sub-blocks through different directions of PCA principal components, the original feature space can be automatically divided into several sub-feature spaces. In addition, a two step extractions of the ICA-PCA information are carried on upon all sub-blocks in order to extract both Gaussian and non-Gaussian information, establishing the new statistics and their statistic limits. Finally, the simulation of TE process shows that the proposed fault detection model is efficient and feasible.

Key words: complex industrial process, automatic partitioning sub-blocks, non-Gaussian, ICA-PCA, fault monitoring

中图分类号:

TP277

衷路生, 何东, 龚锦红, 张永贤. 基于分布式ICA-PCA模型的工业过程故障监测[J]. 化工学报, 2015, 66(11): 4546-4554.

ZHONG Lusheng, HE Dong, GONG Jinhong, ZHANG Yongxian. Fault monitoring of industrial process based on distributed ICA-PCA model[J]. CIESC Journal, 2015, 66(11): 4546-4554.

参考文献 23

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[7]	Zhao Zhonggai(赵忠盖); Liu Fei(刘飞). Factor analysis and its application to process monitoring. Journal of Chemical Industry and Engineering (China)(化工学报),2007,58(4):970-974.
[8]	Zhang, Y.; Ma, C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chem. Eng. Sci. 2011, 66:64-72.
[9]	Song Bing(宋冰);Ma Yuxin(马玉鑫);Fang Yongfeng(方永锋);Shi Hongbo(侍洪波). Fault detection for chemical process based on LSNPE method. CIESC Journal(化工学报), 2014,65(2):0620-0627.
[10]	Lee, J. M.; Yoo, C. K.; Lee, I. B. Statistical process monitoring with independent component analysis. J. Process Control.2004,14: 467-485.
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[12]	Lee, J. M.; Yoo, C. K.; Lee, I. B. Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chem. Eng.Sci. 2004, 59:2995-3006.
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[14]	Ge, Z. Q.; Song, Z. H. Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring.Chemometrics and Intelligent Laboratory Systems.2013, 123:1-8.
[15]	Kano, M.; Hasebe, S.; Hashimoto, I.; Ohno, H. Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Comput. Chem. Eng. 2004, 28: 1157-1166.
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[19]	Cherry, G. A.; Qin, S. J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis. IEEE Trans. Semiconductor Manufacturing.2006, 19:159-172.
[20]	Ge, Z. Q.; Song, Z. H. Two-level multiblock statistical monitoring for plant-wide processes. Korea J. Chem. Eng. 2009, 26,1467-1475.
[21]	Ge, Z. Q.; Song, Z. H. Nonlinear Soft Sensor Development Based on Relevance Vector Machine. Ind. Eng. Chem. Res. 2010, 49:8685-8693.
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[23]	Downs. J.;Vogel E.F.A Plant-wide industrial proeess control problem. Computers &Chemical Engineering.1993,17:245-253.

基于分布式ICA-PCA模型的工业过程故障监测

Fault monitoring of industrial process based on distributed ICA-PCA model

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