Data-driven preventive maintenance for a heterogeneous machine portfolio

被引：3
作者：
Deprez, Laurens [1 ,6 ]
Antonio, Katrien [2 ,3 ]
Arts, Joachim [1 ]
Boute, Robert [2 ,4 ,5 ]
机构：
[1] Univ Luxembourg, Luxembourg Ctr Logist & Supply Chain Management, Esch Sur Alzette, Luxembourg
[2] Katholieke Univ Leuven, Fac Econ & Business, Leuven, Belgium
[3] Univ Amsterdam, Fac Econ & Business, Amsterdam, Netherlands
[4] Vlerick Business Sch, Technol & Operat Management Area, Ghent, Belgium
[5] Flanders Make, VCCM, Lommel, Belgium
[6] 6 Rue Richard Coudenhove Kalergi, L-1359 Luxembourg, Luxembourg
来源：
OPERATIONS RESEARCH LETTERS | 2023年 / 51卷 / 02期
关键词：
Preventive maintenance; Data pooling; Proportional hazards; Small data; SIMULATION; MODEL;
D O I：
10.1016/j.orl.2023.01.006
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码：163 / 170
页数：8
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