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A framework for self-diagnosis and condition monitoring for embedded systems using a SOM-based classifier

UNSPECIFIED (2008) A framework for self-diagnosis and condition monitoring for embedded systems using a SOM-based classifier. In: UNSPECIFIED, ? - ?.

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Abstract

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. © 2008 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2008
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 19 Sep 2013 09:33
Last Modified: 09 Jan 2019 02:15
URI: http://repository.essex.ac.uk/id/eprint/6888

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