Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Guozhu Wang; Jianchang Liu; Yuan Li; Cheng Zhang

doi:10.1016/j.cjche.2016.04.015

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Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy
- Chinese Journal of Chemical Engineering Vol. 24, Issue 7, Pages: 869-880(2016)
- Affiliations：
  
  1. College of Information Science and Engineering, Northeastern University,Shenyang,China,110819
  2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University,Shenyang,China,110819
  3. Information Engineering School, Shenyang University of Chemical Technology,Shenyang,China,110142
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.04.015
  CLC：
- Published：2016
- Accepted：
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Guozhu Wang, Jianchang Liu, Yuan Li, Cheng Zhang. Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy[J]. , 2016, 24(7): 869-880.
DOI：

Guozhu Wang, Jianchang Liu, Yuan Li, Cheng Zhang. Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy[J]. , 2016, 24(7): 869-880. DOI： 10.1016/j.cjche.2016.04.015.

摘要

Abstract

Fault detection and identification are challenging tasks in chemical processes

the aimof which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis (PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods

the PPCA strategy not only reflects the local behavior of process variation in each model (each direction of principal components)

but also improves the monitoring performance through the combination of local monitoring results. Then

a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot

this method locates normal and fault variables effectively

and gives initiatory judgment for ambiguous variables. Finally

the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

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