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Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian TechniquesUncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.
Document ID
20130010482
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Saha, Bhaskar
(Georgia Inst. of Tech. Atlanta, GA, United States)
Goebel, kai
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 27, 2013
Publication Date
December 21, 2007
Subject Category
Electronics And Electrical Engineering
Report/Patent Number
IEEEAC Paper 1361
Meeting Information
Meeting: 2008 IEEE Aerospace Conference
Location: Atlanta, GA
Country: United States
Start Date: March 1, 2008
End Date: March 8, 2008
Sponsors: Institute of Electrical and Electronics Engineers
Distribution Limits
Public
Copyright
Other

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