A Machine Learning Algorithm for Reliability Analysis

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.