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A fuzzy Bayesian based zero-failure accelerated life demonstration test design method

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

Abstract

The zero-failure accelerated degradation demonstration (ADD) test method is employed to verify the product reliability. Generally, the parameter of the acceleration model is assumed to be known in advance. However, the epistemic uncertainty due to the discrepancy of lifetime distribution between the earlier test unit and current products should be taken into consideration. Besides, the epistemic uncertainty due to small sample also leads to an inaccurate estimation. Therefore, it is worth to consider these two epistemic uncertainties above and the random uncertainties caused by random effects in time dimension and unit-to-unit variations. In this paper, a fuzzy Bayesian based zero-failure accelerated degradation demonstration (ADD) test method is proposed. This method takes the three uncertainties into account and is optimized based on the target of minimizing the test cost with a minimum sample size. An electrical power panel zero-failure ADD test is presented to illustrate the proposed method.

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.
Pages180-185
Number of pages6
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

  • Accelerated Degradation Demonstration Test
  • Bayesian
  • Degradation Data
  • High Speed Train System
  • Zero-failure

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