A Copula-Based Multivariate Degradation Analysis for Reliability Prediction

Guanqi Fang, Rong Pan, Yili Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing high-dimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.

Original languageEnglish (US)
Title of host publication2018 Annual Reliability and Maintainability Symposium, RAMS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-January
ISBN (Print)9781538628706
DOIs
StatePublished - Sep 11 2018
Event2018 Annual Reliability and Maintainability Symposium, RAMS 2018 - Reno, United States
Duration: Jan 22 2018Jan 25 2018

Other

Other2018 Annual Reliability and Maintainability Symposium, RAMS 2018
CountryUnited States
CityReno
Period1/22/181/25/18

Keywords

  • copula function
  • degradation data analysis
  • multivariate degradation
  • reliability prediction

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mathematics(all)
  • Computer Science Applications

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    Fang, G., Pan, R., & Hong, Y. (2018). A Copula-Based Multivariate Degradation Analysis for Reliability Prediction. In 2018 Annual Reliability and Maintainability Symposium, RAMS 2018 (Vol. 2018-January). [8463026] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAM.2018.8463026