DocumentCode
3374317
Title
Signal trend identification with fuzzy methods
Author
Wang, Xin ; Wei, Thomas Y C ; Reifman, Jaques ; Tsoukalas, Lefteri H.
Author_Institution
Sch. of Nucl. Eng., Purdue Univ., West Lafayette, IN, USA
fYear
1999
fDate
1999
Firstpage
332
Lastpage
335
Abstract
A fuzzy logic-based methodology for online signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of online signals and classify incoming power plant signals into increasing, decreasing and steady-state trend categories. In order to verify the methodology, a code named PROTREN is developed and tested using plant data. The results indicate that the code is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady-state signal as a transient one
Keywords
fuzzy logic; noise; power plants; signal classification; PROTREN; fuzzy logic; noise; online signal trend identification; power plant signal classification; steady-state signal; transient detection; transient signal; Data mining; Fuzzy logic; Inductors; Laboratories; Power generation; Power system reliability; Signal processing; Steady-state; Testing; Thermal management;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on
Conference_Location
Chicago, IL
ISSN
1082-3409
Print_ISBN
0-7695-0456-6
Type
conf
DOI
10.1109/TAI.1999.809813
Filename
809813
Link To Document