Lifetime Evaluation Method Based on Small Samples and Multi-Source Data

  • Yazeng Chen
  • , Guicui Fu
  • , Hongyan Leng
  • , Ling Zhong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

For the spaceborne components with long lifetime and high reliability, we may get a relatively small number of lifetime samples, but the sample may have more sources. Therefore, how to effectively use these small sample multi-source data is a difficult problem. This paper proposes a comprehensive lifetime assessment method for the spaceborne components which is based on small sample multi-source data and uses Bayesian theory to fuse all data. Firstly, the small sample data of the spaceborne components is regarded as the sample information in the Bayes theory, and the other information of the lifetime data is regarded as the prior information of the Bayes theory. Secondly, according to the method of determining the weight value of the second-type maximum likelihood estimation to obtain the best prior distribution, Multi-source prior information is weighted fusion. Then, the best prior distribution is combined with small sample data to obtain the Bayes posteriori distribution. In the end the comprehensive lifetime assessment of spaceborne component is completed. Finally, the proposed method is used to estimate the lifetime data of a certain type of device.

Original languageEnglish
Title of host publicationProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
EditorsPing Ding, Chuan Li, Shuai Yang, Ping Ding, Rene-Vinicio Sanchez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages659-663
Number of pages5
ISBN (Electronic)9781538653791
DOIs
StatePublished - 4 Jan 2019
Event2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 - Chongqing, China
Duration: 26 Oct 201828 Oct 2018

Publication series

NameProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018

Conference

Conference2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Country/TerritoryChina
CityChongqing
Period26/10/1828/10/18

Keywords

  • Bayes method
  • Lifetime evaluation
  • Multi-source data
  • Spaceborne components

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