A generic method for modeling accelerated life testing data

Haitao Liao, Huairui Guo

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

4 Scopus citations


Accelerated life testing (ALT) is widely used to expedite failures of a product in a short time period for predicting the product's reliability under normal operating conditions. The resulting ALT data are often characterized by a probability distribution, such as Weibull, Lognormal, Gamma distribution, along with a life-stress relationship. However, if the selected failure time distribution is not adequate in describing the ALT data, the resulting reliability prediction would be misleading. This paper proposes a generic method that assists engineers in modeling ALT data. The method uses Erlang-Coxian (EC) distributions, which belong to a particular subset of phase-type (PH) distributions, to approximate the underlying failure time distributions arbitrarily closely. To estimate the parameters of such an EC-based ALT model, two statistical inference approaches are proposed. First, the moment-matching approach (method of moments) is developed to simultaneously match the moments of the EC-based ALT model to the ALT data collected at all test stress levels. In addition, the maximum likelihood estimation (MLE) approach is proposed to handle ALT data with type-I censoring. A numerical example is provided to illustrate the capability of the generic method in modeling ALT data.

Original languageEnglish (US)
Title of host publication59th Annual Reliability and Maintainability Symposium, RAMS 2013 - Proceedings and Tutorials
StatePublished - 2013
Event59th Annual Reliability and Maintainability Symposium, RAMS 2013 - Orlando, FL, United States
Duration: Jan 28 2013Jan 31 2013

Publication series

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


Other59th Annual Reliability and Maintainability Symposium, RAMS 2013
Country/TerritoryUnited States
CityOrlando, FL


  • Erlang-Coxian distribution
  • accelerated life testing
  • maximum likelihood estimation

ASJC Scopus subject areas

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


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