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Graph-based real-time fault diagnosticsA real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.
Document ID
19890006195
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Padalkar, S.
(Vanderbilt Univ. Nashville, TN, United States)
Karsai, G.
(Vanderbilt Univ. Nashville, TN, United States)
Sztipanovits, J.
(Vanderbilt Univ. Nashville, TN, United States)
Date Acquired
September 5, 2013
Publication Date
October 1, 1988
Publication Information
Publication: NASA, Marshall Space Flight Center, Fourth Conference on Artificial Intelligence for Space Applications
Subject Category
Computer Programming And Software
Accession Number
89N15566
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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