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Predicting time to failure using support vector regression
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2010 (English)In: Proceedings of the 1st international workshop and congress on eMaintenance, Luleå tekniska universitet, 2010, 223-226 p.Conference paper (Refereed)
Abstract [en]

Support Vector Machine (SVM) is a new but prospective technique which has been used in pattern recognition, data mining, etc. Taking the advantage of Kernel function, maximum margin and Lanrangian optimization method, SVM has high application potential in reliability data analysis. This paper introduces the principle and some concepts of SVM. One extension of regular SVM named Support Vector Regression (SVR) is discussed. SVR is dedicated to solve continuous problem. This paper uses SVR to predict reliability for repairable system. Taking an equipment from Swedish railway industry as a case, it is shown that the SVR can predict (Time to Failure) TTF accurately and its prediction performance can outperform Artificial Neural Network (ANN).

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2010. 223-226 p.
Research subject
Operation and Maintenance
URN: urn:nbn:se:ltu:diva-29757Local ID: 354a5b20-aec5-11df-a707-000ea68e967bISBN: 978-91-7439-120-6 (PDF)OAI: diva2:1002983
International Workshop and Congress on eMaintenance : 22/06/2010 - 24/06/2010
Godkänd; 2010; 20100823 (ysko)Available from: 2016-09-30 Created: 2016-09-30Bibliographically approved

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