Analyzing step-stress accelerated life testing data using generalized linear models

Jinsuk Lee, Rong Pan

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

In this article the parameter estimation method of Step-Stress Accelerated Life Testing (SSALT) model is discussed by utilizing techniques of Generalized Linear Model (GLM). A multiple progressive SSALT with exponential failure data and right censoring is analyzed. The likelihood function of the SSALT is treated as being a censoring variate with Poisson distribution and the life-stress relationship is defined by a log link function of a GLM. Both the maximum likelihood estimation and the Bayesian estimation of GLM parameters are discussed. The iteratively weighted least squares method is implemented to obtain the maximum likelihood estimation solution. The Bayesian estimation is derived by applying Jeffreys' non-informative prior and the Markov chain Monte Carlo method. Finally, a real industrial example is presented to demonstrate these estimation methods.

Original languageEnglish (US)
Pages (from-to)589-598
Number of pages10
JournalIIE Transactions (Institute of Industrial Engineers)
Volume42
Issue number8
DOIs
StatePublished - Aug 2010

Fingerprint

Maximum likelihood estimation
Testing
Poisson distribution
Parameter estimation
Markov processes
Monte Carlo methods

Keywords

  • Accelerated life testing
  • Bayesian statistics
  • Inference
  • Reliability engineering

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Analyzing step-stress accelerated life testing data using generalized linear models. / Lee, Jinsuk; Pan, Rong.

In: IIE Transactions (Institute of Industrial Engineers), Vol. 42, No. 8, 08.2010, p. 589-598.

Research output: Contribution to journalArticle

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