Fault Detection Based on Fuzzy Neural Networks – Application to Sugar Factory Evaporator

Jan Maciej Kościelny , Andrzej Ostasz , Piotr Wasiewicz

Abstract

Fuzzy and neural process modelling methods for purposes of fault detection have been discussed in the paper. Fuzzy neural networks and TSK models (Takagi-Sugeno-Kang’s models) have been used for modelling. Research results of modelling as well as fault detection algorithms have been presented. Research for the chosen evaporator of evaporation station at “Lublin” sugar factory has been performed. The features of methods mentioned above have been also discussed.
Author Jan Maciej Kościelny (FM / IACR)
Jan Maciej Kościelny,,
- The Institute of Automatic Control and Robotics
, Andrzej Ostasz (FM / IACR)
Andrzej Ostasz,,
- The Institute of Automatic Control and Robotics
, Piotr Wasiewicz (FM / IACR)
Piotr Wasiewicz,,
- The Institute of Automatic Control and Robotics
Journal seriesIFAC Proceedings Volumes, ISSN 1474-6670
Issue year2000
Vol33
No11
Pages343-348
Publication size in sheets0.5
Conference4th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2000 (SAFEPROCESS 2000), 14-06-2000 - 16-06-2000, Budapest, Węgry
Keywords in Englishartificial intelligence, fuzzy modelling, neural-network models, fault detection, computer applications
DOIDOI:10.1016/S1474-6670(17)37381-0
URL https://www.sciencedirect.com/science/article/pii/S1474667017373810
Languageen angielski
Score (nominal)0
Citation count*8 (2015-05-30)
Cite
Share Share

Get link to the record


* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
Back
Confirmation
Are you sure?