Paper Info
Title
Hybrid knowledge-based architecture for building an intelligent nondestructive signal inspection system
Abstract
The paper deals with the methods of implementing an intelligent inspection system for monitoring the health of any device or material using a nondestructive signal. A hybrid knowledge representation and processing architecture is proposed as a solution to problems encountered in the processes of the nondestructive inspection. In the proposed system, to modularize knowledge elicitation and to make knowledge processing reliable, the task of inspection is delegated to two subsystems each of which has a proper knowledge processing scheme to match the properties of its own task. The front-end subsystem which detects the signal patterns (events) for any harmful flaw is built by integrating a fuzzified syntactic pattern recognition concept and a neural network concept. The back-end subsystem which evaluates the characteristics of the events is based upon object-oriented rule base system concepts. The methods of integrating the approaches in the proposed architecture are also proposed in the paper. The proposed architecture is verified by developing and evaluating a prototype which automatically interprets eddy current Lissajous signals to inspect the state of tubes used in nuclear power plants.
Year
DOI
Venue
1995
10.1016/0950-7051(94)00297-V
Knowledge-Based Systems
Keywords
Field
DocType
hybrid knowledge-base architecture,intelligent signal inspection systems,syntactic pattern recognition
Data mining,Architecture,Knowledge representation and reasoning,Computer science,Knowledge processing,Artificial intelligence,Artificial neural network,Syntactic pattern recognition,Knowledge elicitation,Machine learning,Lissajous curve
Journal
Volume
Issue
ISSN
8
1
0950-7051
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Soon Ju Kang15113.16
Yong Rae Kwon2103150.37