Sartain, P. and Hopkins, A.B.T. and McDonald-Maier, K.D. and Howells, W.G.J. (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.
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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. 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: | Jenny Harries |
| Date Deposited: | 14 Apr 2009 12:36 |
| Last Modified: | 31 Jul 2012 11:18 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/13304 (The current URI for this page, for reference purposes) |
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