A review on reliability models with covariates

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.

Value of condition monitoring information for maintenance decision-making

“…Ainsi, l'étude de leurs effets sur le systèmeétudié constitue un enjeu important dans la modélisation des phénomènes de dégradation. Partant de ce point de vue, de nombreux modèles représentant l'influence de covariables ontété développés [13,80,81,114,115,146,151,152,154,157,167,170,213,219,224,245,246,308,310,314].À travers ces modèles, on voit que l'évolution de covariables est souvent décrite par un processus stochastique et les impacts de covariables sur la dégradation continue peuvent se diviser en quatres types principaux (cf. figure 2.1).…”

Estimation of the lifetime distribution of mechatronic systems in the presence of a covariate: A comparison among parametric, semiparametric and nonparametric models

“…This leads in a natural way to a regression problem. In Gorjian et al [4] a review on reliability models with covariates is given. For a fuller treatment we refer the reader to [3,5,6,7,8] and the references given therein.…”

Measurement Plan Optimization for Degradation Test Design based on the Bivariate Wiener Process

“…An appropriate model of the degradation process, such as a degradation path model , a marginal distribution model, a cumulative damage/shock model, a Lévy Process model (e.g. Gamma process), a pure jump process model or a stochastic differential equation model, is identified; see, for example, . The lifetime and reliability level of the product are then obtained by solving for the distribution of the first passage time, i.e.…”