skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Signal trend identification with fuzzy methods.

Abstract

A fuzzy-logic-based methodology for on-line signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line 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.

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab., IL (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
11931
Report Number(s):
ANL/RE/CP-99798
TRN: US0102294
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Information, Intelligence and Systems, Washington, DC (US), 11/01/1999--11/03/1999; Other Information: PBD: 19 Aug 1999
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; P CODES; FUZZY LOGIC; POWER PLANTS; TRANSIENTS; DETECTION; ON-LINE MEASUREMENT SYSTEMS; SIGNALS

Citation Formats

Reifman, J., Tsoukalas, L. H., Wang, X., and Wei, T. Y. C. Signal trend identification with fuzzy methods.. United States: N. p., 1999. Web.
Reifman, J., Tsoukalas, L. H., Wang, X., & Wei, T. Y. C. Signal trend identification with fuzzy methods.. United States.
Reifman, J., Tsoukalas, L. H., Wang, X., and Wei, T. Y. C. Thu . "Signal trend identification with fuzzy methods.". United States. https://www.osti.gov/servlets/purl/11931.
@article{osti_11931,
title = {Signal trend identification with fuzzy methods.},
author = {Reifman, J. and Tsoukalas, L. H. and Wang, X. and Wei, T. Y. C.},
abstractNote = {A fuzzy-logic-based methodology for on-line signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line 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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {8}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: