Predicting time to failure using support vector regression

Projekt:

JVTC

Sammanfattning:
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).

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Författare: Yuan Fuqing ; Uday Kumar
Utgivare: Luleå tekniska universitet
Utgivningsdatum: 2010
Diarienummer: TRV 2011/58769
ISBN: 978-91-7439-120-6
Antal sidor: 4
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs