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Title: Statistically qualified neuro-analytic failure detection method and system

Abstract

An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.

Inventors:
 [1];  [2];  [3]
  1. (Aurora, IL)
  2. (Idaho Falls, ID)
  3. (Naperville, IL)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL
Sponsoring Org.:
USDOE
OSTI Identifier:
969042
Patent Number(s):
6,353,815
Application Number:
09/186,306
Assignee:
The United States of America as represented by the United States Department of Energy (Washington, DC) CHO
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Patent
Country of Publication:
United States
Language:
English

Citation Formats

Vilim, Richard B., Garcia, Humberto E., and Chen, Frederick W.. Statistically qualified neuro-analytic failure detection method and system. United States: N. p., 2002. Web.
Vilim, Richard B., Garcia, Humberto E., & Chen, Frederick W.. Statistically qualified neuro-analytic failure detection method and system. United States.
Vilim, Richard B., Garcia, Humberto E., and Chen, Frederick W.. Sat . "Statistically qualified neuro-analytic failure detection method and system". United States. https://www.osti.gov/servlets/purl/969042.
@article{osti_969042,
title = {Statistically qualified neuro-analytic failure detection method and system},
author = {Vilim, Richard B. and Garcia, Humberto E. and Chen, Frederick W.},
abstractNote = {An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2002},
month = {3}
}

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