A deep learning predictive model for selective maintenance optimization. (March 2022)

• Record Type: Journal Article
• Authors: Hesabi, Hadis; Nourelfath, Mustapha; Hajji, Adnène
• Is Part Of: Reliability engineering & system safety. Volume 219(2022)
• Publication Date: 2022-03
• Subjects: Remaining useful life -- Deep learning -- Predictive models -- Selective maintenance -- Optimization; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108191
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Developing a predictive model for selective maintenance optimization. Using long short-term memory network to estimate the remaining useful lifetime. Data-driven selective maintenance is better than model-based. Imperfect vs perfect preventive maintenance in predictive selective maintenance. Abstract: 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.