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A Framework for Self-Diagnosis and Condition Monitoring for Embedded Systems Using a SOM-Based Classifier

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4 Author(s)
Sartain, P. ; Dept. of Comput. & Electron. Syst., Essex Univ., Colchester ; Hopkins, A.B.T. ; McDonald-Mair, K.D. ; Howells, G.

This paper presents a system level framework for system-on-chip (SoC) based embedded devices that may include adaptive and reconfigurable elements. Current development support and debugging solutions are highly dependant on off-line post-mortem style inspection, and even those that utilise tracing for real-time and schedule-critical systems rely on external development tools and environments. This new framework introduces an AI-lead infrastructure that has the potential to reduce much of the development effort while complementing existing debugging circuits. Specifically this paper investigates how to use a Kohonen self-organising map (SOM) as a classifier, and shows a preliminary investigation into how to determine the quality of a map after training. This classifier is a first step in diagnosing failure, degradation and anomalies (i.e. provides condition monitoring) in an embedded system from a system level point of view, and in the larger task of self-diagnosis of an embedded system.

Published in:

Adaptive Hardware and Systems, 2008. AHS '08. NASA/ESA Conference on

Date of Conference:

22-25 June 2008