AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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A Kernel Density Estimate-Based Approach to Component Goodness Modeling
Nuno Cardoso, Rui Abreu

Last modified: 2013-06-30

Abstract


Intermittent fault localization approaches account for the fact that faulty components may fail intermittently by considering a parameter (known as goodness) that quantifies the probability that faulty components may still exhibit correct behavior. Current, state-of-the-art approaches (1) assume that this goodness probability is context independent and (2) do not provide means for integrating past diagnosis experience in the diagnostic mechanism. In this paper, we present a novel approach, coined Non-linear Feedback-based Goodness Estimate (NFGE), that uses kernel density estimations (KDE) to address such limitations. We evaluated the approach with both synthetic and real data, yielding lower estimation errors, thus increasing the diagnosis performance.

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