Neural network based system for equipment surveillance

Vilim, Richard B; Gross, Kenneth C; Wegerich, Stephan W

Title: Neural network based system for equipment surveillance

Abstract

A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

Inventors:

Vilim, Richard B ^[1]; Gross, Kenneth C ^[2]; Wegerich, Stephan W ^[3]

Aurora, IL
Bolingbrook, IL
Glendale Hts., IL

Issue Date:: 1998-01-01

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

OSTI Identifier:: 871513

Patent Number(s):: 5745382

Assignee:: ARCH Development Corporation (Chicago, IL)

Patent Classifications (CPCs):: Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y02 - TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE Y02E - REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION

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

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Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y02 - TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE Y02E - REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
Y02E30/30 - Nuclear fission reactors

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
Y10S706/904 - Manufacturing or machine, e.g. agricultural machinery, machine tool

G - PHYSICS G05 - CONTROLLING G05B - CONTROL OR REGULATING SYSTEMS IN GENERAL
G05B23/024 - {Quantitative history assessment, e.g. mathematical relationships between available data

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
Y10S706/906 - Process plant

G - PHYSICS G21 - NUCLEAR PHYSICS G21D - NUCLEAR POWER PLANT
G21D3/001 - {Computer implemented control}

G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
G06K9/00536 - {Classification

Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y02 - TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE Y02E - REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
Y02E30/00 - Energy generation of nuclear origin

G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G06N3/08 - Learning methods

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DOE Contract Number:: W-31109-ENG-38

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: neural; network; based; equipment; surveillance; method; performing; transient; signals; industrial; device; ascertain; operating; involves; steps; reading; memory; training; data; determining; weighting; values; achieving; target; outputs; close; output; inadequate; wavelet; parameters; determined; yield; desired; set; providing; characteristic; process; comparing; evaluate; process signals; neural network; industrial process; network based; training data; performing surveillance; desired set; neural net; transient signal; /706/702/

Citation Formats


                    Vilim, Richard B, Gross, Kenneth C, and Wegerich, Stephan W. Neural network based system for equipment surveillance.  United States: N. p., 1998. 
        Web.

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                    Vilim, Richard B, Gross, Kenneth C, & Wegerich, Stephan W. Neural network based system for equipment surveillance.  United States.

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                    Vilim, Richard B, Gross, Kenneth C, and Wegerich, Stephan W. Thu .  
        "Neural network based system for equipment surveillance".  United States.  https://www.osti.gov/servlets/purl/871513.

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@article{osti_871513,

  title        = {Neural network based system for equipment surveillance},

  author       = {Vilim, Richard B and Gross, Kenneth C and Wegerich, Stephan W},

  abstractNote = {A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {1998},

  month        = {1}

}

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Works referenced in this record:

Review of Neural Networks for Speech Recognition
journal, March 1989

Lippmann, Richard P.
Neural Computation, Vol. 1, Issue 1
https://doi.org/10.1162/neco.1989.1.1.1

Modular Construction of Time-Delay Neural Networks for Speech Recognition
journal, March 1989

Waibel, Alex
Neural Computation, Vol. 1, Issue 1
https://doi.org/10.1162/neco.1989.1.1.39

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Title: Neural network based system for equipment surveillance

Abstract

Citation Formats

Review of Neural Networks for Speech Recognition journal, March 1989

Modular Construction of Time-Delay Neural Networks for Speech Recognition journal, March 1989

Review of Neural Networks for Speech Recognition
journal, March 1989

Modular Construction of Time-Delay Neural Networks for Speech Recognition
journal, March 1989