A statistical method of accelerated life testing based on fuzzy theory

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

Abstract

Failure data in accelerated life test are imprecise due to kind of subjectivity. This makes the assessment of testing lack of accuracy. A statistical model of accelerated life testing based on fuzzy theory is proposed in this paper. The model aims to describe the subjectivity of failure data and give a fuzzy interval assessment of lifetime and reliability comparing to the traditional point estimation. Firstly, triangular membership function is chosen to describe Type II censored data and their a-level cut sets are settled under acceptable level. Then, the statistical modelling for ALT data is established based on fuzzy theory by integrating maximum likelihood estimation to determine the membership functions of parameters. And using Particle Swarm Optimization algorithm calculates their fuzzy evaluation values. Finally, the proposed method is verified by the simulation study.

Original languageEnglish
Title of host publicationICRMS 2014 - Proceedings of 2014 10th International Conference on Reliability, Maintainability and Safety
Subtitle of host publicationMore Reliable Products, More Secure Life
EditorsYunfei En, Chunyang Ji
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages746-749
Number of pages4
ISBN (Electronic)9781479919925
DOIs
StatePublished - 13 May 2014
Event2014 10th International Conference on Reliability, Maintainability and Safety, ICRMS 2014 - Guangzhou, China
Duration: 6 Aug 20148 Aug 2014

Publication series

NameICRMS 2014 - Proceedings of 2014 10th International Conference on Reliability, Maintainability and Safety: More Reliable Products, More Secure Life

Conference

Conference2014 10th International Conference on Reliability, Maintainability and Safety, ICRMS 2014
Country/TerritoryChina
CityGuangzhou
Period6/08/148/08/14

Keywords

  • Accelerated life testing
  • Fuzzy theory
  • Maximum likelihood estimation
  • Particle swarm optimization
  • Reliability

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