Comparison study on general methods for modeling lifetime data with covariates

Haitao Liao, Samira Karimi

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

2 Scopus citations

Abstract

Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.

Original languageEnglish (US)
Title of host publication2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538603703
DOIs
StatePublished - Oct 20 2017
Externally publishedYes
Event8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017 - Harbin, China
Duration: Jul 9 2017Jul 12 2017

Other

Other8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017
CountryChina
CityHarbin
Period7/9/177/12/17

Keywords

  • accelerated life testing
  • phase-type distributions

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Transportation

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  • Cite this

    Liao, H., & Karimi, S. (2017). Comparison study on general methods for modeling lifetime data with covariates. In 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings [8079122] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM.2017.8079122