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Proceedings Paper

Multiple fault diagnosis using multiple context spaces
Author(s): Gyesung Lee; Gautam Biswas
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Paper Abstract

Diagnostic problem solving is a major application area of knowledge-based system research. However, most of the current approaches, both heuristic and model-based, are designed to identify single faults, and do not generalize easily to multiple fault diagnosis without exhibiting exponential behavior in the amount of computation required. In this paper, we employ a decomposition approach based on system configuration to generate an efficient algorithm for multiple fault diagnosis. The basic idea of the algorithm is to reduce the inherent combinatorial explosion that occurs in generating multiple faults by partitioning the circuit into groups that correspond to output measurement points. Rules are developed for combining candidates from individual groups, and forming consistent sets of minimal candidates.

Paper Details

Date Published: 1 January 1990
PDF: 9 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21057
Show Author Affiliations
Gyesung Lee, Vanderbilt Univ. (United States)
Gautam Biswas, Vanderbilt Univ. (United States)

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

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