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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.ORCID iD: 0000-0002-4107-0991
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Center for Intelligent Maintenance Systems (IMS), Cincinnati, OH.
Center for Intelligent Maintenance Systems (IMS), Cincinnati, OH.
2012 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, no 3, p. 2-8Article in journal (Refereed) Published
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. To test the system's RUL, degradation speed is evaluated by computing the minimal distance based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data on a different time horizon. The final RUL of a specific component can be estimated and global RUL information can be obtained by aggregating the multiple RUL estimations using a density estimation method

Place, publisher, year, edition, pages
2012. Vol. 15, no 3, p. 2-8
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-5954Local ID: 425163f3-e071-4271-9376-beac3bdbea6fOAI: oai:DiVA.org:ltu-5954DiVA: diva2:978830
Note
Godkänd; 2012; 20130912 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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