Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem. Issue 10 (3rd October 2021)

• Record Type: Journal Article
• Authors: Pisacane, Ornella; Potena, Domenico; Antomarioni, Sara; Bevilacqua, Maurizio; Emanuele Ciarapica, Filippo; Diamantini, Claudia
• Is Part Of: Engineering optimization. Volume 53:Issue 10(2021)
• Page Start: 1752
• Page End: 1771
• Publication Date: 2021-10-03
• Subjects: Augmented ε-constraint -- large neighbourhood search -- predictive maintenance -- mathematical programming; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2020.1823381
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : In systems with many components that are required to be constantly active, such as refineries, predicting the components that will break in a time interval after a stoppage may significantly increase their reliability. However, predicting the set of components to be repaired is a challenging task, especially when several conditions ( e.g. breakage probability, repair time and cost) have to be considered simultaneously. A data-driven predictive maintenance policy is proposed for maximizing the system reliability and minimizing the maximum repair time, considering both budget and human resources constraints. Therefore, a data-driven algorithm is designed for extracting component breakage probabilities. Then, two bi-objective optimization approaches are proposed for determining the set of components to repair. The former is based on the formulation of a bi-objective mixed integer linear programming model solved through the AUGMEnted ε -CONstraint (AUGMECON) method. The latter implements a bi-objective large neighbourhood search, outperforming the first approach.