Distributed Prognostic Health Management with Gaussian Process Regression
Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A 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.
Complete Metadata
| bureauCode |
[ "026:00" ] |
|---|---|
| identifier | DASHLINK_727 |
| issued | 2013-05-09 |
| landingPage | https://c3.nasa.gov/dashlink/resources/727/ |
| programCode |
[ "026:029" ] |
