A Framework for Self-Diagnosis and Condition Monitoring of Embedded Hardware using a SOM-Based Classifier

Sartain, P. and Hopkins, Andrew B.T. and McDonald-Maier, Klaus D. and Howells, Gareth (2008) A Framework for Self-Diagnosis and Condition Monitoring of Embedded Hardware using a SOM-Based Classifier. In: Third NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2008), June 2008, Noordwijk, Netherlands. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper presents a system level framework for System-on-Chip (SoC) based embedded devices that may include adaptive and reconfigurable elements. Cut-rent 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 v on external development tools and environments. This new framework introduces an AI-lead infrastructure that A as 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.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: J. Harries
Date Deposited: 14 Apr 2009 12:36
Last Modified: 19 Jun 2014 15:32
Resource URI: https://kar.kent.ac.uk/id/eprint/13304 (The current URI for this page, for reference purposes)
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