![]() The PHM Group collaborates with industry and research partners to develop advanced sensors for diagnostics and prognostics applications. In addition, optimal maintenance planning and business case development to assess the return on investment associated with the application of PHM to systems is being researched by the group. The group is evaluating the use of intelligent reasoning technologies to model and manage the life cycle of electronic products. Work in the areas of reliability modeling and prediction, pattern recognition, time series forecasting, machine learning, and fusion technologies is ongoing. The goal of the group is to develop novel ways to identify anomalies and patterns within very large data sets containing multiple parameters both qualitative and quantitative and has developed real-time reduced-order modeling for failure prediction. It is pioneering the use of a fusion approach, which combines physics of failure and data-driven methods for accurate prognostics and diagnostics. The group is using physics-based models along with empirical models for prognostics. The research focuses on computational algorithms, advanced sensors and data collection techniques, condition-based maintenance, PHM for the application of in-situ diagnostics and prognostics. The PHM Group conducts research and development of prognostics and health management applications for electronic products and systems, as well as systems-of-systems. Our approaches for PHM implementation include: (1) the use of expendable devices, such as canaries and fuses that fail earlier than the host product to provide advance warning of failure (2) monitoring and reasoning of parameters that are precursors to impending failure, such as shifts in performance parameters and (3) modeling of stress and damage in electronic parts and structures utilizing exposure conditions (e.g., usage, temperature, vibration, radiation) to compute accumulated damage. The Prognostics and Health Management (PHM) Group has a multi-faceted approach to PHM focused on demonstrating that health monitoring can be implemented using a variety of methodologies, tools, and analyzing techniques for effective prognostics. Prognostics and Health Management (PHM) permits the evaluation of a system’s reliability in its actual life-cycle conditions.Health Management utilizes prognostic information to make decisions related to safety, condition-based maintenance, ensuring adequate inventory, and product life extension.Prognostics is the process of monitoring the health of a product and predicting its remaining useful life (RUL) by assessing the extent of deviation or degradation from its expected state of health in its expected usage conditions.Welcome to the Prognostics and Health Management Groupĭedicated to providing a research and knowledge base to support the advancement of diagnostics, prognostics, and system health management.
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