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A Machine Learning Algorithm for Reliability Analysis
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DOI：10.1109/TR.2020.3011653.png)
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Abstract
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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.
Keywords:
Cross-validation
importance reliability measures
isotonic smoothing
logistic regression (LR)
quasi-likelihood
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