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Remaining useful life estimation using time trajectory tracking and support vector machines
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
NSFI/UCR Center for Intelligent Maintenance System (IMS), University of Cincinnati.
NSFI/UCR Center for Intelligent Maintenance System (IMS), University of Cincinnati.
2012 (English)In: 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012): 18–20 June 2012, Huddersfield, UK, IOP Publishing Ltd , 2012, Article number 012063- p.Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IOP Publishing Ltd , 2012. Article number 012063- p.
Series
, Journal of Physics Conference Series, ISSN 1742-6588 ; 1
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-39701DOI: 10.1088/1742-6596/364/1/012063Local ID: e8d125aa-ac86-49e6-af08-d0a4c28af511OAI: oai:DiVA.org:ltu-39701DiVA: diva2:1013218
Conference
International Congress on Condition Monitoring and Diagnostic Engineering Management : 18/06/2012 - 20/06/2012
Note
Validerad; 2012; Bibliografisk uppgift: Article number 012063; 20120529 (ysko)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

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