Analysis of degradation process with measurement errors

Rong Pan, Wendai Wang

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

3 Scopus citations

Abstract

Degradation tests are often applied on highly reliable products when the product performance can be repeatedly measured. In this paper, we compare two common types of degradation models - a nonlinear regression model and a stochastic process model. Particularly, we discuss the effects of measurement error on model parameter estimation and model selection. Using an example of photovoltaic product degradation, we (1) demonstrate the use of linear models for estimating model parameters, and (2) provide a hypothesis test for the statistical significance of product performance degradation. It shows that some forms of measurement errors, such as the drifting error of tester, can be easily incorporated into the analysis of stochastic degradation models.

Original languageEnglish (US)
Title of host publicationRAMS 2014 - Proceedings 2014
Subtitle of host publicationThe 60th Annual Reliability and Maintainability Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479928477
DOIs
StatePublished - Jan 1 2014
Event60th Annual Reliability and Maintainability Symposium, RAMS 2014 - Colorado Springs, CO, United States
Duration: Jan 27 2014Jan 30 2014

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
ISSN (Print)0149-144X

Other

Other60th Annual Reliability and Maintainability Symposium, RAMS 2014
CountryUnited States
CityColorado Springs, CO
Period1/27/141/30/14

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Keywords

  • Degradation modeling
  • Hypothesis test
  • Linear models
  • Wiener process

ASJC Scopus subject areas

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

Cite this

Pan, R., & Wang, W. (2014). Analysis of degradation process with measurement errors. In RAMS 2014 - Proceedings 2014: The 60th Annual Reliability and Maintainability Symposium [6798513] (Proceedings - Annual Reliability and Maintainability Symposium). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAMS.2014.6798513