LTSA and combined index based MICA and PCA process monitoring and application

JIANG Wei; WANG Xin; WANG Zhenlei

doi:10.11949/j.issn.0438-1157.20151374

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LTSA and combined index based MICA and PCA process monitoring and application

更新时间：2025-12-10

- LTSA and combined index based MICA and PCA process monitoring and application
- CIESC Journal Issue 12, Pages: 4895-4903(2015)
- 作者机构：
  
  1. 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237
  2. 上海交通大学电工与电子技术中心,上海,200240
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151374
  CLC： P277
- Online First：05 December 2015，
  
  Published：05 December 2015
- 稿件说明：
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JIANG Wei, WANG Xin, WANG Zhenlei. LTSA and combined index based MICA and PCA process monitoring and application[J]. CIESC Journal, 2015, (12): 4895-4903.
DOI：

JIANG Wei, WANG Xin, WANG Zhenlei. LTSA and combined index based MICA and PCA process monitoring and application[J]. CIESC Journal, 2015, (12): 4895-4903. DOI： 10.11949/j.issn.0438-1157.20151374.

摘要

独立成分分析(ICA)方法主要被用来对线性非高斯过程进行监控

为了提高对非高斯过程的监控效果

则利用过程数据信息对ICA的监控指标进行了改进

提出了一种改进的独立成分分析(MICA)方法。许多实际工业过程数据都具有非线性、非高斯与高斯混合分布的特点

为此提出了一种基于LTSA和MICA与PCA联合指标的过程监控的方法。首先采用局部切空间排列(LTSA)算法对样本数据进行非线性降维

然后分别用MICA和PCA方法得到非高斯与高斯统计量

对其进行加权得到新的统计量

并被用于过程监控。最后将该方法应用在田纳西-伊斯曼(TE)过程和乙烯裂解炉的过程监控中

证明了该方法的有效性。

Abstract

Independent component analysis (ICA) method is mainly used to monitor linear and non-Gaussian process

a method named modified independent analysis (MICA) is proposed to improve the non-Gaussian process monitoring performance. The method uses the information of process data to modify the monitoring process monitoring performance. The method uses the information of process data to modify the monitoring index of ICA. Many industrial process variables have characteristics of nonlinear

non-Gaussian and Gaussian mixture distribution. A method which based on LTSA algorithm and the combined index is proposed to solve these problems of the industrial process. Firstly

the local tangent space alignment (LTSA) algorithm is used to achieve the nonlinear dimensionality reduction of sample data. Then MICA and PCA methods are used to obtain non-Gaussian and Gaussian statistics

and the new statistic

which is weighted by these two statistics

is used for process monitoring. Finally

the proposed method has been applied to monitor the Tennessee-Eastman(TE) process and the ethylene cracking furnace to show its efficiency.

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