A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

Fuqiang Sun, Xiaoyang Li, Haitao Liao, Xiankun Zhang

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system's reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component's power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

Original languageEnglish (US)
JournalAdvances in Mechanical Engineering
Volume9
Issue number1
DOIs
Publication statusPublished - Jan 1 2017
Externally publishedYes

    Fingerprint

Keywords

  • Bayesian least-squares support vector machine
  • confidence bands
  • microwave component
  • Remaining useful life

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this