Remaining useful life estimation using time trajectory tracking and support vector machines

Projekt:

JVTC

Sammanfattning:
In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at different time horizon. Therefore, the final RUL of a specific component can be estimated and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

Författare: Diego Galar ; Uday Kumar ; J. Lee ; W. Zhao
Utgivare: IOP Publishing Ltd
Utgivningsdatum: 2012
Diarienummer: TRV 2011/58769
ISSN: 1742-6588
Antal sidor: 10
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs