J
Javad Barabady
Researcher at University of Tromsø
Publications - 54
Citations - 1112
Javad Barabady is an academic researcher from University of Tromsø. The author has contributed to research in topics: Reliability (statistics) & Maintainability. The author has an hindex of 15, co-authored 52 publications receiving 987 citations. Previous affiliations of Javad Barabady include Tromsø University College & Luleå University of Technology.
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Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran
Javad Barabady,Uday Kumar +1 more
TL;DR: A case study describing reliability and availability analysis of the crushing plant number 3 at Jajarm Bauxite Mine in Iran shows that the reliability analysis is very useful for deciding maintenance intervals.
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Maintainability analysis considering time-dependent and time-independent covariates
TL;DR: The Cox regression model and its extension in the presence of time-dependent covariates for determining maintainability is developed and a simple case study is used to demonstrate how the model can be applied in a real case.
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Availability allocation through importance measures
Javad Barabady,Uday Kumar +1 more
TL;DR: In this article, the authors define availability importance measures in order to calculate the criticality of each component or subsystem from the availability point of view and demonstrate the application of such importance measures for achieving optimal resource allocation to arrive at the best possible availability.
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An approach for prediction of petroleum production facility performance considering Arctic influence factors
TL;DR: The proportional repair model (PRM) is developed in order to predict repair rate in Arctic conditions based on the proportional hazard model (PHM) and a simple case study is used to demonstrate how the proposed approach can be applied.
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Application of reliability models with covariates in spare part prediction and optimization – A case study
TL;DR: The aim of this paper is to demonstrate the application of the available reliability models with covariates in the field of spare part predictions by means of a case study.