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Title: Industrial process surveillance system

Abstract

A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

Inventors:
 [1];  [2];  [3];  [4]
  1. (Bolingbrook, IL)
  2. (Glendale Heights, IL)
  3. (Naperville, IL)
  4. (Idaho Falls, ID)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL
OSTI Identifier:
871615
Patent Number(s):
US 5764509
Assignee:
University of Chicago (Chicago, IL) ANL
DOE Contract Number:
W-31109-ENG-38
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
industrial; process; surveillance; method; monitoring; data; source; generating; time; varying; sources; processing; obtain; correlation; determining; range; learned; normal; operation; generate; expected; values; comparing; current; identify; closest; set; modeled; pattern; alarm; detecting; deviation; normalcy; time varying; normal operation; industrial process; data source; industrial data; data sources; obtain time; varying data; time correlation; generating time; process surveillance; data pattern; /700/706/

Citation Formats

Gross, Kenneth C., Wegerich, Stephan W., Singer, Ralph M., and Mott, Jack E.. Industrial process surveillance system. United States: N. p., 1998. Web.
Gross, Kenneth C., Wegerich, Stephan W., Singer, Ralph M., & Mott, Jack E.. Industrial process surveillance system. United States.
Gross, Kenneth C., Wegerich, Stephan W., Singer, Ralph M., and Mott, Jack E.. Thu . "Industrial process surveillance system". United States. doi:. https://www.osti.gov/servlets/purl/871615.
@article{osti_871615,
title = {Industrial process surveillance system},
author = {Gross, Kenneth C. and Wegerich, Stephan W. and Singer, Ralph M. and Mott, Jack E.},
abstractNote = {A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 1998},
month = {Thu Jan 01 00:00:00 EST 1998}
}

Patent:

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  • A system and method are disclosed for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting amore » deviation from normalcy. 96 figs.« less
  • A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation frommore » normalcy.« less
  • A method and system for automatically establishing operational parameters of a statistical surveillance system. The method and system performs a frequency domain transition on time dependent data, a first Fourier composite is formed, serial correlation is removed, a series of Gaussian whiteness tests are performed along with an autocorrelation test, Fourier coefficients are stored and a second Fourier composite is formed. Pseudorandom noise is added, a Monte Carlo simulation is performed to establish SPRT missed alarm probabilities and tested with a synthesized signal. A false alarm test is then emperically evaluated and if less than a desired target value, thenmore » SPRT probabilities are used for performing surveillance.« less
  • A method and system for monitoring an industrial process and a sensor are disclosed. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs 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 subtractingmore » the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test. 17 figs.« less
  • A method and system are disclosed for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs 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 subtractingmore » the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test. 17 figs.« less