A graphical technique and penalized likelihood method for identifying and estimating infant failures

Shuai Huang, Rong Pan, Jing Li

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Field failure data often exhibit extra heterogeneity as early failure data may have quite different distribution characteristics from later failure data. These infant failures may come from a defective subpopulation instead of the normal product population. Many exiting methods for field failure analyses focus only on the estimation for a hypothesized mixture model, while the model identification is ignored. This paper aims to develop efficient, accurate methods for both detecting data heterogeneity, and estimating mixture model parameters. Mixture distribution detection is achieved by applying a mixture detection plot (MDP) on field failure observations. The penalized likelihood method, and the expectation-maximization (EM) algorithm are then used for estimating the components in the mixture model. Two field datasets are employed to demonstrate and validate the proposed approach.

Original languageEnglish (US)
Article number5545476
Pages (from-to)650-660
Number of pages11
JournalIEEE Transactions on Reliability
Volume59
Issue number4
DOIs
StatePublished - Dec 1 2010

Keywords

  • Expectation maximization
  • infant mortality
  • mixture detection plot
  • mixture distribution

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

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