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Title: Combined expert system/neural networks method for process fault diagnosis

Abstract

A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

Inventors:
 [1];  [2]
  1. (Westchester, IL)
  2. (Downers Grove, IL)
Issue Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL
OSTI Identifier:
870030
Patent Number(s):
5442555
Assignee:
Argonne National Laboratory (Argonne, IL) ANL
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
combined; expert; neural; networks; method; process; fault; diagnosis; two-level; hierarchical; approach; operating; employs; function-oriented; level; component; characteristic-oriented; decision-making; procedure; structured; decreasing; intelligence; increasing; precision; diagnostic; knowledge; overall; including; wide; variety; plant; transients; functional; behavior; components; classifies; malfunctions; function; narrow; focus; particular; set; faulty; responsible; detected; misbehavior; limits; scope; detailed; characteristics; trained; artificial; uniquely; identify; classifying; abnormal; condition; data; failure; hypothesized; anomaly; structure; successively; oriented; determined; diagnostic method; artificial neural; process including; neural network; wide variety; neural networks; overall process; uniquely identify; abnormal condition; faulty component; normal condition; fault diagnosis; neural net; process fault; /701/376/702/706/

Citation Formats

Reifman, Jaques, and Wei, Thomas Y. C. Combined expert system/neural networks method for process fault diagnosis. United States: N. p., 1995. Web.
Reifman, Jaques, & Wei, Thomas Y. C. Combined expert system/neural networks method for process fault diagnosis. United States.
Reifman, Jaques, and Wei, Thomas Y. C. Sun . "Combined expert system/neural networks method for process fault diagnosis". United States. https://www.osti.gov/servlets/purl/870030.
@article{osti_870030,
title = {Combined expert system/neural networks method for process fault diagnosis},
author = {Reifman, Jaques and Wei, Thomas Y. C.},
abstractNote = {A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1995},
month = {1}
}

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