Large-scale selective maintenance optimization using bathtub-shaped failure rates. (4th August 2020)

• Record Type: Journal Article
• Authors: Ikonen, Teemu J.; Mostafaei, Hossein; Ye, Yixin; Bernal, David E.; Grossmann, Ignacio E.; Harjunkoski, Iiro
• Is Part Of: Computers & chemical engineering. Volume 139(2020)
• Publication Date: 2020-08-04
• Subjects: Reliability -- Optimization -- Selective maintenance -- Component replacement -- Component repair -- Failure rate; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106876
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Linking of lifetime data analysis into selective maintenance optimization Selective maintenance optimization based on bathtub-shaped failure rates Efficiency improvement 1: replacement preclusion based on the infant mortality period Efficiency improvement 2: convexified selective maintenance optimization models Applicable to large-scale systems with up to 700 to 1000 components Graphical abstract: Abstract: Engineering systems are typically maintained during planned, or unplanned, downtimes in between operation periods. If the duration of the downtime or the budget of the maintenance is an active constraint, all desired maintenance actions cannot be conducted. Seeking of the optimal subset of maintenance actions is referred to as selective maintenance optimization . In this work, we link the statistical analysis of lifetime data into selective maintenance optimization, focusing on datasets with bathtub-shaped failure rates. We also propose two improvements to the efficiency of mixed integer non-linear programming (MINLP)-based selective maintenance optimization. The first is the preclusion of component replacements that, due to the infant mortality period of the component, reduce the reliability. The second is the convexification of two MINLP models, involving only replacement, or replacement and repair, actions. The improvements enable our MINLP-based methods to tackle large-scale selective maintenance optimization problems with up to 700 to 1000 system components.