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One-class support vector machines with a bias constraint and its application in system reliability prediction

Published online by Cambridge University Press:  03 May 2019

Zhengwei Hu ,
Zhangli Hu  and
Xiaoping Du
Zhengwei Hu
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Zhangli Hu
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Xiaoping Du*
Affiliation:
Department of Mechanical and Energy Engineering, Indiana University – Purdue University Indianapolis, Indianapolis, IN 46202, USA
*
Author for correspondence: Xiaoping Du, E-mail: [email protected]
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Abstract

Support vector machine (SVM) methods are widely used for classification and regression analysis. In many engineering applications, only one class of data is available, and then one-class SVM methods are employed. In reliability applications, the one-class data may be failure data since the data are recorded during reliability experiments when only failures occur. Different from the problems handled by existing one-class SVM methods, there is a bias constraint in the SVM model in this work and the constraint comes from the probability of failure estimated from the failure data. In this study, a new one-class SVM regression method is proposed to accommodate the bias constraint. The one class of failure data is maximally separated from a hypersphere whose radius is determined by the known probability of failure. The proposed SVM method generates regression models that directly link the states of failure modes with design variables, and this makes it possible to obtain the joint probability density of all the component states of an engineering system, resulting in a more accurate prediction of system reliability during the design stage. Three examples are given to demonstrate the effectiveness of the new one-class SVM method.

Keywords

First-order reliability methodoptimizationsupport vector machinessystem reliabilitystatistical dependence
Type
Research Article
Information
AI EDAM , Volume 33 , Issue 3 , August 2019 , pp. 346 - 358
Copyright
Copyright © Cambridge University Press 2019 

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