Observed and unobserved heterogeneity in failure data analysis

In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. I...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability Journal of risk and reliability, 2022-02, Vol.236 (1), p.194-207
Main Authors: Zaki, Rezgar, Barabadi, Abbas, Barabady, Javad, Nouri Qarahasanlou, Ali
Format: Article
Language:English
Subjects:
Data analysis
Failure analysis
Heterogeneity
Reliability analysis
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Summary:In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to inaccurate reliability modeling, and consequently, wrong operation and maintenance decisions. There is a lack of a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. Here, the unobserved covariates will be analyzed using frailty models. A case study will illustrate the application of the framework.
ISSN:1748-006X
1748-0078
1748-0078
DOI:10.1177/1748006X211022538