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Development of robust prognostic for digital electronic system health management will improve maintainability and operational readiness for many industries with products ranging from enterprise network servers to military aircraft. The emerging digital PHM technology discussed in this paper can be applied in a myriad of applications ranging from on-board/on-wing deployment to integration into ground based support systems such as automated test equipment (ATE) or logistic planning tools. Techniques from a variety of disciplines are required to develop an effective, robust, and technically sound health management system for digital electronics. The presented technical approach integrates collaborative diagnostic and prognostic techniques from engineering disciplines including statistical reliability, damage accumulation modeling, physics-of-failure modeling, signal processing & feature extraction, and automated reasoning algorithms. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data with probabilistic component models to achieve the best decisions on the overall health of the digital system. A comprehensive component prognostic capability can be achieved by utilizing a combination of health monitoring data and model-based estimates. Both board and component level minimally-invasive and purely internal data acquisition methods are paired with model-based assessments to demonstrate this approach to digital component health state awareness.