Hybrid system for fault diagnosis using scanned input: A tutorial
- Oak Ridge National Lab., TN (USA)
- University of Southwestern Louisiana, Lafayette, LA (USA)
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.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6094163
- Report Number(s):
- CONF-9104165-1; ON: DE91009558
- 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
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Related Subjects
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