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小木虫: 金币+0.5, 给个红包，谢谢回帖
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[ORGINASITION]University of Maryland
Centre for Advanced Life Cycle Engineering
[WEBSITES]
http://www.prognostics.umd.edu/index.html
[SUMMARY]
Prognostics is the process of predicting the future reliability of a product by assessing the extent of deviation or degradation of a product from its expected normal operating conditions. Health monitoring is a process of measuring and recording the extent of deviation and degradation from a normal operating condition.
The 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. 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 PHM Prognostics group conducts research and development of prognostics and health management applications for electronic products and systems, as well as systems-of-systems. The research focuses on computational algorithms, advanced sensors and data collection techniques, condition-based maintenance, prognostics and health management for the application of in-situ diagnostics and prognostics. The group is using physics based models along with empirical models for prognostics. We are pioneering the use of a fusion approach, which combines physics of failure and data driven methods for accurate prognostics and diagnostics.
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. Work in the areas of reliability modeling and prediction, pattern recognition, time series forecasting, machine learning, and fusion technologies is ongoing. The prognostics group is evaluating the use of intelligent reasoning technologies to model and manage the life cycle of electronic products. 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 PHM group collaborates with industry and research partners to develop advanced sensors for diagnostics and prognostics applications. Applications such as tamper proof low-cost autonomous sensors that incorporate wireless communication, high onboard memory capacity and can be attached to any product with minimal interference to the functioning of that product are being developed. The PHM group enables real time prognostics and health management of electronic products in their application environment. |
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