Statistically qualified neuroanalytic failure detection method and system
Abstract
An apparatus and method for monitoring a process involve development and application of a statistically qualified neuroanalytic (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 neuroanalytic 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 neuroanalytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuroanalytic 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 online monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.
 Inventors:

 Aurora, IL
 Idaho Falls, ID
 Naperville, IL
 Issue 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)
 DOE Contract Number:
 W31109ENG38
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Vilim, Richard B, Garcia, Humberto E, and Chen, Frederick W. Statistically qualified neuroanalytic failure detection method and system. United States: N. p., 2002.
Web.
Vilim, Richard B, Garcia, Humberto E, & Chen, Frederick W. Statistically qualified neuroanalytic failure detection method and system. United States.
Vilim, Richard B, Garcia, Humberto E, and Chen, Frederick W. Sat .
"Statistically qualified neuroanalytic failure detection method and system". United States. https://www.osti.gov/servlets/purl/969042.
@article{osti_969042,
title = {Statistically qualified neuroanalytic 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 neuroanalytic (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 neuroanalytic 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 neuroanalytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuroanalytic 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 online 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}
}