Title :
Visual method for process monitoring and its application to Tennessee Eastman challenge problem
Author :
Gu, Yi-Ming ; Zhao, W-Hong ; Hui Wang
Author_Institution :
Inst. of Syst. Eng., Zhejiang Univ., Hangzhou, China
Abstract :
Online process monitoring is extremely important for the successful operation of any process. A visual data-based method suitable for online monitoring of complex systems is proposed. The self-organizing map is used to project a high-dimensional vector of process data onto a 2D visualization space in which different process conditions are represented by different regions. The process state can be indicated by the trajectory in visualization space. The effectiveness of the proposed method is illustrated by the application on the Tennessee Eastman process. Online monitoring and fault detection can be carried in a more intuitionistic and practical manner by using this method.
Keywords :
chemical industry; control engineering computing; data visualisation; fault diagnosis; large-scale systems; process monitoring; production engineering computing; self-organising feature maps; 2D visualization space; Tennessee Eastman challenge problem; complex systems; fault detection; high-dimensional vector; online process monitoring; self-organizing map; visual data-based method; Application software; Computerized monitoring; Data visualization; Fault detection; Knowledge engineering; Large-scale systems; Modems; Neural networks; Space technology; Systems engineering and theory;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1380378
