A local and global statistics pattern analysis method and its application to process fault identification

Hanyuan Zhang; Xuemin Tian; Xiaogang Deng; Lianfang Cai

doi:10.1016/j.cjche.2015.09.004

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A local and global statistics pattern analysis method and its application to process fault identification

过程系统工程与过程安全 | Updated：2026-01-06

- A local and global statistics pattern analysis method and its application to process fault identification
- A local and global statistics pattern analysis method and its application to process fault identification
- 中国化学工程学报（英文版） 2015年23卷第11期页码：1782-1792
- Affiliations：
  
  College of Information and Control Engineering, China University of Petroleum (East China),Qingdao,China,266580
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.09.004
  中图分类号：
- 纸质出版：2015
- Accepted：
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Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, 等. A local and global statistics pattern analysis method and its application to process fault identification[J]. 中国化学工程学报（英文版）, 2015,23(11):1782-1792.

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai. A local and global statistics pattern analysis method and its application to process fault identification[J]. Chin.J.Chem.Eng., 2015, 23(11): 1782-1792.
Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, 等. A local and global statistics pattern analysis method and its application to process fault identification[J]. 中国化学工程学报（英文版）, 2015,23(11):1782-1792. DOI： 10.1016/j.cjche.2015.09.004.

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai. A local and global statistics pattern analysis method and its application to process fault identification[J]. Chin.J.Chem.Eng., 2015, 23(11): 1782-1792. DOI： 10.1016/j.cjche.2015.09.004.

摘要

Abstract

Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently

a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However

thesemethods omit the local information

which is also important for process monitoring and fault diagnosis. In this paper

a local and global statistics pattern analysis (LGSPA) method

which integrates SPA framework and locality preserving projections within the PCA

is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection

two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables

an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics

a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

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