January 1, 2010.
The definitive version is available at http://dx.doi.org/.
Prognostics and health management (PHM) algorithms track the health condition of a system and make an assessment of the time until which this system can perform within desired specifications. These algorithms require development of fault growth models and data analysis on measurements available from the system. During the course of this program I will engage in the above activities by means of two research projects. The model development will be done for Lithium Iron Phosphate (LiFePO4) batteries. By understanding the physical and chemical processes within the battery a model for charge capacity degradation will be developed for these batteries that are used in hybrid electric vehicles (HEV), plug-in HEV, laptop, aircraft, etc. This effort will include conducting the experiments in the lab and collecting the data. Contingent of available time data analysis and algorithm development may follow. In a parallel effort, data analysis exposure will be gained by working on fatigue cycling data on carbon-carbon composites. This analysis extracts features of material degradation as it is subjected to fatigue cycling. This experiments will help develop fault propagation models for composite materials that are expected to be used for aerospace structures such as spacecraft and aircraft fuselage. PHM of these systems is a critical step in keeping these systems safe and running efficiently.
NASA Ames Research Center (ARC)