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 :
بازگشت