Condition monitoring and fault prediction via an adaptive neural network

Tan, Shing Chiang and Lim, Chee Peng 2000, Condition monitoring and fault prediction via an adaptive neural network, in TENCON 2000 : Proceedings : Intelligent systems and technologies for the new millennium, IEEE, Piscataway, N. J., pp. I-13-I-17.

Attached Files
Name Description MIMEType Size Downloads

Title Condition monitoring and fault prediction via an adaptive neural network
Author(s) Tan, Shing Chiang
Lim, Chee Peng
Conference name Trends in Electronics Conference (2000 : Kuala Lumpur, Malaysia)
Conference location Kuala Lumpur, Malaysia
Conference dates 24-27 Sept. 2000
Title of proceedings TENCON 2000 : Proceedings : Intelligent systems and technologies for the new millennium
Editor(s) [Unknown]
Publication date 2000
Conference series Trends in Electronics Conference
Start page I-13
End page I-17
Total pages 5
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) fuzzy ARTMAP
condition monitoring
pruning algorithm
Summary This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.
ISBN 0780363558
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048763

Document type: Conference Paper
Collection: Institute for Frontier Materials
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 5 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 36 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 26 Sep 2012, 09:24:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.