System Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

The use of Kohonen self-organizing maps in process monitoring

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Vermasvuori, M. ; Dept. of Process Control & Autom., Helsinki Univ. of Technol., Espoo, Finland ; Enden, P. ; Haavisto, S. ; Jamsa-Jounela, S.-L.

Process monitoring and fault diagnosis have been studied widely in recent years, and the number of industrial applications with encouraging results has grown rapidly. In the case of complex processes a computer aided monitoring enhances operators' possibilities to run the process economically. In this paper a fault diagnosis system is described and some application results from the Outokumpu Harjavalta smelter are discussed. The system monitors process states using neural networks (Kohonen self-organizing maps, SOM) in conjunction with heuristic rules, which are also used to detect equipment malfunctions.

Published in:

Intelligent Systems, 2002. Proceedings. 2002 First International IEEE Symposium  (Volume:3 )

Date of Conference: