A Deep-Learning-Based Framework to Predict the Reliability of Multicomponent Repairable Systems in a Closed-Loop Supply Chain

Abstract

In this article, we develop a data-driven approach to predict the reliability of multicomponent repairable systems, considering component dependencies. We estimate component reliability functions from system-level time-to-failure data without prior knowledge of the system structure and use these estimates to generate training data for a deep long short-term memory network. This leads to system reliability prediction and addresses uncertainties through quantile regression. Validated through simulations of 500 systems and real-world data from GE HealthCare magnetic resonance imaging (MRI) machines, our model outperforms traditional methods (such as Cox model and random survival forest) in terms of accuracy, particularly for complex systems, by effectively learning from uncertainties.