Proceedings of 2013 International Conference on Information Science and Computer Applications

Research of Boiler Fault Diagnosis Based on Fuzzy Neural Network

Authors
Yifeng Wu, Xiaoqi Lin
Corresponding Author
Yifeng Wu
Available Online October 2013.
DOI
10.2991/isca-13.2013.44How to use a DOI?
Keywords
boiler; fuzzy neural network; fault diagnosis
Abstract

Boiler system has the characteristics of high complexity, strong simultaneity, multiple measuring points, multiple faults, and the traditional fault diagnosis method can not meet the requirements. A fault diagnosis model based on fuzzy neural network is built by the combination of fuzzy logic technology and the improved BP neural network algorithm. The model is used to fault diagnosis and it can better solve the ambiguity, simultaneity and correlation of the boiler fault.Through analyzing the fault example of high temperature superheater damage, and the diagnosis results of the application of the model are verified that they are consistent with the actual operation conditions.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of 2013 International Conference on Information Science and Computer Applications
Series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
10.2991/isca-13.2013.44
ISSN
1951-6851
DOI
10.2991/isca-13.2013.44How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yifeng Wu
AU  - Xiaoqi Lin
PY  - 2013/10
DA  - 2013/10
TI  - Research of Boiler Fault Diagnosis Based on Fuzzy Neural Network
BT  - Proceedings of 2013 International Conference on Information Science and Computer Applications
PB  - Atlantis Press
SP  - 255
EP  - 262
SN  - 1951-6851
UR  - https://doi.org/10.2991/isca-13.2013.44
DO  - 10.2991/isca-13.2013.44
ID  - Wu2013/10
ER  -