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

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.