High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

Ying Tian; Wenli Du; Feng Qian

doi:10.1016/j.cjche.2015.03.007

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High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

过程系统工程与过程安全 | Updated：2026-01-06

- High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process
- Chinese Journal of Chemical Engineering Vol. 23, Issue 9, Pages: 1509-1517(2015)
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.03.007
  CLC：
- Published：2015
- Accepted：
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Ying Tian, Wenli Du, Feng Qian. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process[J]. , 2015, 23(9): 1509-1517.
DOI：

Ying Tian, Wenli Du, Feng Qian. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process[J]. , 2015, 23(9): 1509-1517. DOI： 10.1016/j.cjche.2015.03.007.

摘要

Abstract

Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas

liquid and solid. To monitor the process and to improve the product quality

as well as to visualize the fault type clearly

a fault diagnosis method based on selforganizing map (SOM) and high dimensional feature extraction method

local tangent space alignment (LTSA)

is proposed. In this method

LTSA can reduce the dimension and keep the topology information simultaneously

and SOMdistinguishes various states on the outputmap.Monitoring results of PX oxidation reaction process indicate that the LTSA-SOM can well detect and visualize the fault type.

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