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Proceedings Paper

Learning diagnostics expert system for plasma arc welding machines
Author(s): Ganesan Vaidyanathan; S. Sharma; Frederick E. Petry
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Paper Abstract

A versatile model to diagnose the performance failures of a plasma arc welding machine is presented in this paper. The understanding of the functional structure of the machine under consideration plays a vital role in the use of an expert system as a tool. The torch is the focal point of action in such a welding machine. Therefore, the fundamental analysis is based on the torch with the rest of the functions built around it. We have introduced a dynamic learning concept based on the recurrence of problems. The learning model includes a facility index, a function based on the complexity of the troubleshooting associated with the machine under consideration. The increase in frequency of problem events can supersede the facility index. The probability parameters at each node of the structure is computed and dynamically updated at the end of each diagnostic operation. The system is designed to be used by a maintenance technician and tailored for an expert to interact and alter the probability parameters in the logical reasoning domain. The model can be implemented either on new machines without any historical background or on old machines with a troubleshooting history.

Paper Details

Date Published: 1 January 1990
PDF: 8 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21056
Show Author Affiliations
Ganesan Vaidyanathan, Tulane Univ. (United States)
S. Sharma, Tulane Univ. (United States)
Frederick E. Petry, Tulane Univ. (United States)

Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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