Candidate ordering and elimination in model-based fault diagnosis

Authors:
Jiah-Shing Chen;Sargur N. Srihari
Affiliations:
Department of Computer Science, State University of New York at Buffalo, Buffalo, New York;Department of Computer Science, State University of New York at Buffalo, Buffalo, New York
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Year:
1989

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Abstract

A major step in model-based fault diagnosis is the generation of candidate submodules which might be responsible for the observed symptom of malfunction. After the candidates are determined, each subrnodule can then be examined in turn. It is useful to be able to choose the most likely candidate to focus on first so that the faulty parts can be located sooner. We propose here a systematic method for initial candidate ordering that takes into account the structure of the device and the discrepancy in outputs between the observed and expected values. We also give effective methods for a system to adjust its focus according to new information acquired during diagnosis. Under the single fault assumption, the average length of diagnosis (number of submodules evaluated) is O(logm), where m is the number of submodules.