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Data-driven preventive maintenance for a heterogeneous machine portfolio
Deprez, Laurens Antonio, Katrien Arts, Joachim Boute, Robert
Deprez, Laurens
Antonio, Katrien
Arts, Joachim
Boute, Robert
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Journal article with impact factor
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Supervisor
Publication Year
2023-03
Journal
Operations Research Letters
Book
Publication Volume
51
Publication Issue
2
Publication Begin page
163
Publication End page
170
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Abstract
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.
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Keywords
4901 Applied Mathematics, 49 Mathematical Sciences, Machine Learning and Artificial Intelligence