DocumentCode
1807805
Title
Combining neural networks, fuzzy logic, and Kalman filtering in an oil leak detector for underground electric power cables
Author
Fischer, Daniel ; Szabados, B. ; Poehlman, Skip
Author_Institution
Kinectrics, Toronto, Ont., Canada
Volume
3
fYear
2004
fDate
18-20 May 2004
Firstpage
2099
Abstract
This paper presents some of the issues that must be dealt with during the implementation of an oil leak detector in underground power cables. By using a very limited number of sensors, the detector must perform a considerable amount of signal processing in order to achieve reasonable security and dependability. Three original solutions making use of Neural Network, Fuzzy Logic, and Kalman Filtering are presented and compared.
Keywords
Kalman filters; fuzzy logic; leak detection; neural nets; oil filled cables; signal processing; underground cables; Kalman filtering; dielectric fluid; disturbance canceller; fuzzy logic; high pressure fluid filled transmission line; neural networks; oil leak detector; oil pressure changes; signal processing; temperature variations; underground electric power cables; Detectors; Filtering; Fuzzy logic; Kalman filters; Leak detection; Lubricating oils; Neural networks; Petroleum; Signal processing; Underground power cables;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE
ISSN
1091-5281
Print_ISBN
0-7803-8248-X
Type
conf
DOI
10.1109/IMTC.2004.1351504
Filename
1351504
Link To Document