The Prognostics Center of Excellence (PCoE) at Ames Research Center provides an umbrella for prognostic technology development, specifically addressing prognostic technology gaps within the application areas of aeronautics and space exploration. The PCoE is currently investigating damage propagation mechanisms on select safety-critical actuators for transport-class aircraft, damage mechanisms on aircraft wiring insulation, and damage propagation mechanisms for critical electrical and electronic components in avionic equipment. We are also in the process of extending a testbed that will allow the comparative analysis of different prognostic algorithms. In addition, data collected from aging processes will be made available to the research community (see link to data repository below).
Next-generation aircraft such as this morphing wing concept will experience new and unknown faults and failure modes, and will benefit from integrated health management.
The common thread among the various avenues of prognostic technology development is the investigation of physics-of-failure at the component level. Modeling damage initiation and propagation at this level is a key element in describing component health. Just as important is the investment of resources into algorithm development to provide the estimates for remaining component life and for uncertainty management.
Some of the challenges that we are interested in tackling include:
To that end, we will employ tools from engineering, statistics, and machine learning. Specifically, we draw upon expertise in:
Prognostic Data Repository
One of the common bottlenecks in prognostic algorithm development is the availability of data that allows the comparison and benchmarking of algorithm performance. This data repository is geared towards easing that bottleneck by making available prognostic data sets to the research community.
+ Visit Prognostic Data Repository
Systems Health, Analytics, Resilience, and Physics-modeling (SHARP) Lab
The PCoE makes use of laboratory facilities designed to test, measure, evaluate, and mature diagnostic and prognostic health management technologies. A number of hardware-in-the-loop testbeds and associated measurement equipment allow for controlled, repeatable, safe injection of faults.
+ Visit SHARP
Goebel, Kai, Ph.D.
Open Source Products Generalized Software Architecture for Prognostics Prognostic Models Prognostic Algorithms
Annual Conference of the PHM Society
St. Petersburg, FL
Oct 2 - Oct 5, 2017
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