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A Machine Learning Algorithm for Reliability Analysis
dc.contributor.author | Gámiz Pérez, María Luz | |
dc.contributor.author | Navas Gómez, Fernando Jesús | |
dc.contributor.author | Raya Miranda, Rocío | |
dc.date.accessioned | 2024-02-02T13:23:26Z | |
dc.date.available | 2024-02-02T13:23:26Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/10481/88039 | |
dc.description.abstract | In this article, we build a statistical model able to predict the reliability of the system based on a dataset. Our objective is double. On the one hand, we aim at constructing a function that classifies the system in one of the two categories (operative or failed) based on the knowledge of components states. On the other hand, we present a statistical test to decide the order of importance of components in terms of the effect each one has on the system performance. We present a supervised algorithm involving isotonic smooth logistic regression and cross-validation techniques. Our method is completely data-driven not lying in any parametric assumptions. The method is illustrated through an extensive simulation study. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | A Machine Learning Algorithm for Reliability Analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.identifier.doi | 10.1109/TR.2020.3011653 | |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |