A Multi-objective Framework for Designing Accelerated Degradation Tests under Wiener Process Model

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

Abstract

In this paper, a novel multi-objective framework for designing accelerated degradation tests (ADTs) is developed under Wiener process model. The framework aims at providing aids to balance prediction accuracy and mechanism equivalence of a specific ADT. To achieve this, a new optimization criterion, named as the MV-optimization criterion, is firstly proposed by using the maximum likelihood theory. Then, under the cost constraint, a nonlinear optimization problem is constructed. Finally, a comparison is carried out between our multi-objective optimal plan and other traditional single-objective plans through the case of electrical connector. Results show that the MV-optimization plan has better properties from the perspective of improving the rationality of prediction and the robustness of test plans.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
PublisherIEEE Computer Society
Pages1709-1713
Number of pages5
ISBN (Electronic)9781538667866
DOIs
StatePublished - 2 Jul 2018
Event2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand
Duration: 16 Dec 201819 Dec 2018

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2019-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Country/TerritoryThailand
CityBangkok
Period16/12/1819/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Accelerated degradation test
  • mechanism equivalence
  • multi-objective optimization
  • prediction accuracy

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