A deep learning predictive model for selective maintenance optimization

This paper develops a predictive selective maintenance framework using deep learning and mathematical programming. We consider a multi-component system executing consecutive production missions with scheduled intermission maintenance breaks. During the intermission breaks, several maintenance actions can improve each component's remaining useful life at a given cost. An optimization model is developed to identify a subset of maintenance actions to perform on the components. The objective is to minimize the total cost under intermission break time limitation. The total cost is composed of maintenance and failure costs; it depends on the success probabilities of the subsequent missions. To estimate these probabilities, the optimization model interacts with a long short-term memory network. The resulting predictive selective maintenance framework is validated using a benchmarking data set provided by NASA for a Modular Aero-Propulsion System Simulation of a Commercial Turbofan Engine. Its performance is highlighted when compared with the model-based approach. The results illustrate the advantages of the predictive selective maintenance framework to predict the health condition of each component with accuracy and deal with the selective maintenance of series systems.

Automated summary

This paper introduces a predictive selective maintenance framework for multi-component systems using deep learning and mathematical programming. The framework accurately predicts the health condition of each component and selects maintenance actions accordingly. The performance of the framework is validated using a benchmarking data set provided by NASA.