Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

陈心怡; 颜学峰

doi:10.1016/S1004-9541(13)60469-3

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Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

过程系统工程与过程安全 | Updated：2026-01-06

- Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis
- Chinese Journal of Chemical Engineering Vol. 21, Issue 4, Pages: 382-3871004-954160469-3(2013)
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(13)60469-3
  CLC：
- Published：2013
- Accepted：
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CHEN Xinyi, YAN Xuefeng . Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis[J]. Chin.J.Chem.Eng., 2013, 21(4): 382-387.
DOI：

CHEN Xinyi, YAN Xuefeng . Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis[J]. Chin.J.Chem.Eng., 2013, 21(4): 382-387. DOI： 10.1016/S1004-9541(13)60469-3.

摘要

Abstract

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field

in which the effective visualization method is still challenging. In order to get a better visualization effect

a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA

SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

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