A Machine Learning Algorithm for Reliability Analysis Gámiz Pérez, María Luz Navas Gómez, Fernando Jesús Raya Miranda, Rocío 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. 2024-02-02T13:23:26Z 2024-02-02T13:23:26Z 2021 info:eu-repo/semantics/article https://hdl.handle.net/10481/88039 10.1109/TR.2020.3011653 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/embargoedAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional IEEE