Application of Prognostic Health Management in Digital Electronic Systems

Development of robust prognostics for digital electronic system health management will improve device reliability and maintainability for many industries with products ranging from enterprise network servers to military aircraft. Techniques from a variety of disciplines is 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 and 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 digital components and systems. A comprehensive component prognostic capability can be achieved by utilizing a combination of health monitoring data and model-based estimates used when no diagnostic indicators are present. Both board and component level minimally-invasive and purely internal data acquisition methods will be paired with model-based assessments to demonstrate this approach to digital component health state awareness.