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

ZHONG Lusheng; HE Dong; GONG Jinhong; ZHANG Yongxian

doi:10.11949/j.issn.0438-1157.20150546

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

更新时间：2025-12-10

- Fault monitoring of industrial process based on distributed ICA-PCA model
- CIESC Journal Issue 11, Pages: 4546-4554(2015)
- 作者机构：
  
  华东交通大学电气学院,江西,南昌,330013
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20150546
  CLC： P277
- Online First：05 November 2015，
  
  Published：05 November 2015
- 稿件说明：
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ZHONG Lusheng, HE Dong, GONG Jinhong, et al. Fault monitoring of industrial process based on distributed ICA-PCA model[J]. CIESC Journal, 2015, (11): 4546-4554.
DOI：

ZHONG Lusheng, HE Dong, GONG Jinhong, et al. Fault monitoring of industrial process based on distributed ICA-PCA model[J]. CIESC Journal, 2015, (11): 4546-4554. DOI： 10.11949/j.issn.0438-1157.20150546.

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。

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.

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