skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Hybrid system for fault diagnosis using scanned input: A tutorial

Abstract

It is well known that expert systems are useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process analysis. Traditionally, expert system applications are limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, however, represent an emerging technology capable of rapid recognition of patterned behavior without regard to mathematical complexity. Although useful in problem identification, neutral networks are not very efficient in pointing to in-depth solutions and typically do not promote a profound understanding of the problem or the reasoning behind its solutions. This paper explores the potential for expanding the scope of expert system applications by combining expert systems with neural networks. Any computer system that is partly an expert system and partly a neural network can be called a hybrid system. This pairing is a natural one because where one system falls short the other excels. Imprecise (or even incomplete) data can submitted to a neutral network for classification. Once these data are classified, the results are not always as precise as need dictates. It is at this point that the expert system can be invoked to do what it does best: takemore » definite and complete, but general, input and produce a definite and precise output. This paper presents a relatively new approach--one that expands the scope of artificial intelligence (AI) applications by combining expert systems and neural networks to form a hybrid system. A general methodology for developing hybrid systems is given. The methodology and merits of developing hybrid systems are illustrated through a case study.« less

Authors:
;  [1];  [2]
  1. Oak Ridge National Lab., TN (USA)
  2. University of Southwestern Louisiana, Lafayette, LA (USA)
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (USA)
Sponsoring Org.:
USDOE; USDOE, Washington, DC (USA)
OSTI Identifier:
6094163
Report Number(s):
CONF-9104165-1
ON: DE91009558
DOE Contract Number:  
AC05-84OR21400
Resource Type:
Conference
Resource Relation:
Conference: National American Institute of Chemical Engineers (AIChE) meeting, Houston, TX (USA), 7-11 Apr 1991
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; EXPERT SYSTEMS; NEURAL NETWORKS; FAULT TREE ANALYSIS; OPTICAL SCANNERS; ARTIFICIAL INTELLIGENCE; COMPUTER ARCHITECTURE; HYBRID SYSTEMS; KNOWLEDGE BASE; ELECTRONIC EQUIPMENT; EQUIPMENT; OPTICAL EQUIPMENT; SYSTEM FAILURE ANALYSIS; SYSTEMS ANALYSIS; 990200* - Mathematics & Computers

Citation Formats

Ferrada, J J, Osborne-Lee, I W, and Grizzaffi, P A. Hybrid system for fault diagnosis using scanned input: A tutorial. United States: N. p., 1991. Web.
Ferrada, J J, Osborne-Lee, I W, & Grizzaffi, P A. Hybrid system for fault diagnosis using scanned input: A tutorial. United States.
Ferrada, J J, Osborne-Lee, I W, and Grizzaffi, P A. Tue . "Hybrid system for fault diagnosis using scanned input: A tutorial". United States.
@article{osti_6094163,
title = {Hybrid system for fault diagnosis using scanned input: A tutorial},
author = {Ferrada, J J and Osborne-Lee, I W and Grizzaffi, P A},
abstractNote = {It is well known that expert systems are useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process analysis. Traditionally, expert system applications are limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, however, represent an emerging technology capable of rapid recognition of patterned behavior without regard to mathematical complexity. Although useful in problem identification, neutral networks are not very efficient in pointing to in-depth solutions and typically do not promote a profound understanding of the problem or the reasoning behind its solutions. This paper explores the potential for expanding the scope of expert system applications by combining expert systems with neural networks. Any computer system that is partly an expert system and partly a neural network can be called a hybrid system. This pairing is a natural one because where one system falls short the other excels. Imprecise (or even incomplete) data can submitted to a neutral network for classification. Once these data are classified, the results are not always as precise as need dictates. It is at this point that the expert system can be invoked to do what it does best: take definite and complete, but general, input and produce a definite and precise output. This paper presents a relatively new approach--one that expands the scope of artificial intelligence (AI) applications by combining expert systems and neural networks to form a hybrid system. A general methodology for developing hybrid systems is given. The methodology and merits of developing hybrid systems are illustrated through a case study.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1991},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: