This paper discusses the use of knowledge base system software for microcomputers to aid repairmen in diagnosing electrical failures in complex mining machinery. The knowledge base is constructed to allow the user to input initial symptoms of the failed machine, and the most probable cause of failure is traced through the knowledge base, with the software requesting additional information such as voltage or resistance measurements as needed. Although the case study presented is for an underground mining machine, results have application to any industry using complex machinery. Two commercial expert-system development tools (M.1™ and Insight 2+™) and an Al language (Turbo Prolog™) are discussed with emphasis on ease of application and suitability for this study.
|Number of pages||9|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - May 11 1987|
|Event||Applications of Artificial Intelligence V 1987 - Orlando, United States|
Duration: May 18 1987 → May 22 1987
Bibliographical noteFunding Information:
This project was funded under a grant from University of Alabama's School of Mines and Energy Development. The authors also wish to acknowledge the contributions of the Department of Mineral Engineering and The College of Engineering at the University for release time during the academic year to work on the project.
© 1987 SPIE. All rights reserved.
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering