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.

中文翻译：

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用于可靠性分析的机器学习算法


在本文中，我们构建了一个统计模型，能够根据数据集预测系统的可靠性。我们的目标是双重的。一方面，我们的目标是构建一个函数，根据组件状态的知识将系统分类为两个类别（运行或失败）之一。另一方面，我们提出了一种统计测试，根据每个组件对系统性能的影响来确定组件的重要性顺序。我们提出了一种涉及等渗平滑逻辑回归和交叉验证技术的监督算法。我们的方法完全由数据驱动，不依赖于任何参数假设。该方法通过广泛的模拟研究进行了说明。