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Title: Expert system for testing industrial processes and determining sensor status

Abstract

A method and system for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

Inventors:
 [1];  [2]
  1. Bolingbrook, IL
  2. Naperville, IL
Issue Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
OSTI Identifier:
871601
Patent Number(s):
5761090
Assignee:
University of Chicago (Chicago, IL)
Patent Classifications (CPCs):
G - PHYSICS G05 - CONTROLLING G05B - CONTROL OR REGULATING SYSTEMS IN GENERAL
Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
expert; testing; industrial; processes; determining; sensor; status; method; monitoring; process; minimum; pairs; evaluating; operation; technique; generating; signal; characteristic; variable; obtaining; signals; associated; physical; difference; function; obtained; arithmetic; pair; time; frequency; domain; transformation; obtain; fourier; modes; describing; composite; residual; subtracting; free; nonwhite; noise; analyzed; statistical; probability; ratio; nonwhite noise; signal characteristic; industrial processes; frequency domain; difference function; industrial process; residual function; probability ratio; process variable; composite function; signals associated; sensor status; statistical probability; obtain fourier; modes describing; domain transformation; dual function; fourier modes; arithmetic difference; physical variable; determining sensor; white noise; /714/706/

Citation Formats

Gross, Kenneth C, and Singer, Ralph M. Expert system for testing industrial processes and determining sensor status. United States: N. p., 1998. Web.
Gross, Kenneth C, & Singer, Ralph M. Expert system for testing industrial processes and determining sensor status. United States.
Gross, Kenneth C, and Singer, Ralph M. Tue . "Expert system for testing industrial processes and determining sensor status". United States. https://www.osti.gov/servlets/purl/871601.
@article{osti_871601,
title = {Expert system for testing industrial processes and determining sensor status},
author = {Gross, Kenneth C and Singer, Ralph M},
abstractNote = {A method and system for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1998},
month = {6}
}

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