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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.
Original language | English |
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Article number | 106876 |
Number of pages | 21 |
Journal | Computers and Chemical Engineering |
Volume | 139 |
DOIs | |
Publication status | Published - 4 Aug 2020 |
MoE publication type | A1 Journal article-refereed |
Keywords
- reliability
- optimization
- selective maintenance
- component replacement
- component repair
- failure rate
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Dive into the research topics of 'Large-scale selective maintenance optimization using bathtub-shaped failure rates'. Together they form a unique fingerprint.Projects
- 1 Finished
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Synergistic and intelligent process optimization
Harjunkoski, I., Mohammadi, M., Ikonen, T. & Mostafaei, H.
01/01/2018 → 31/12/2019
Project: Academy of Finland: Other research funding