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A review on reliability models with covariates

  • Nima Gorjian
  • Lin Ma
  • Murthy Mittinty
  • Prasad Yarlagadda
  • Yong Sun

Abstract

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semiparametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.

Keywords

Failure Time Covariate Model Baseline Hazard Function Accelerate Failure Time Model Failure Time Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Nima Gorjian
    • 1
    • 2
  • Lin Ma
    • 1
    • 2
  • Murthy Mittinty
    • 3
  • Prasad Yarlagadda
    • 2
  • Yong Sun
    • 1
    • 2
  1. 1.Cooperative Research Centre for Integrated Engineering Asset Management (CIEAM)BrisbaneAustralia
  2. 2.School of Engineering SystemsQueensland University of Technology (QUT)BrisbaneAustralia
  3. 3.School of Mathematical SciencesQueensland University of Technology (QUT)BrisbaneAustralia

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