DocumentCode
2063681
Title
Distributed prognostic health management with gaussian process regression
Author
Saha, Sankalita ; Saha, Bhaskar ; Saxena, Abhinav ; Goebel, Kai
Author_Institution
MCT/NASA ARC, Moffett Field, CA, USA
fYear
2010
fDate
6-13 March 2010
Firstpage
1
Lastpage
8
Abstract
Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. 12A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper, we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.
Keywords
Gaussian processes; aerospace industry; battery management systems; condition monitoring; particle filtering (numerical methods); regression analysis; wireless sensor networks; Gaussian process regression; battery prognostics; communication pattern; distributed GPR based prognostics algorithm; distributed prognostic health management; distributed prognostics architecture design; optimal distributed prognostics algorithm; particle filter; wireless sensor network; Algorithm design and analysis; Batteries; Clustering algorithms; Computer architecture; Gaussian processes; Ground penetrating radar; Intelligent sensors; NASA; Sensor systems; Wireless sensor networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Aerospace Conference, 2010 IEEE
Conference_Location
Big Sky, MT
ISSN
1095-323X
Print_ISBN
978-1-4244-3887-7
Electronic_ISBN
1095-323X
Type
conf
DOI
10.1109/AERO.2010.5446841
Filename
5446841
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