"One-Class Support Vector Machines with a Bias Constraint and its Appli" by Zhengwei Hu, Zhangli Hu et al.
 

One-Class Support Vector Machines with a Bias Constraint and its Application in System Reliability Prediction

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.

Department(s)

Mechanical and Aerospace Engineering

Comments

National Science Foundation, Grant CMMI 1562593

Keywords and Phrases

First-order reliability method; optimization; statistical dependence; support vector machines; system reliability

International Standard Serial Number (ISSN)

1469-1760; 0890-0604

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Cambridge University Press, All rights reserved.

Publication Date

01 Aug 2019

Link to Full Text

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