The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/f533593a-c27e-4b4e-afe0-5354f93c5fb1

Eggertsson, Ragnar; Eruguz, Ayse Sena; Basten, Rob; Maillart, Lisa (2024): Code underlying the publication: Maintenance Optimization for Multi-Component Systems with a Single Sensor. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/f533593a-c27e-4b4e-afe0-5354f93c5fb1.v1

Software

• Other Engineering
• Engineering

Condition-based maintenance, Inspection planning, Maintenance, Multi-component systems, Partially observable Markov decision process

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ragnar Eggertsson , Ayse Sena Eruguz , Rob Basten , Lisa Maillart

This is the code used to generate the results for the paper Maintenance Optimization for Multi-Component Systems with a Single Sensor by Ragnar Eggertsson, Ayse Sena Eruguz, Rob Basten, and Lisa M. Maillart. The paper introduces a novel model to optimize maintenance interventions for a multi-component system with a single sensor. The model is formulated as a partially observable Markov decision process. The Python script in this repository implements the algorithm, discussed in the paper, that solves the model. This algorithm is a type of incremental pruning algorithm. Running this algorithm generates the results presented in the section Illustrative Examples.

• 2024-09-12 first online, published, posted

4TU.ResearchData

text_file/.txt images/.png script/.py

Maintenance optimization for multi-component systems with a single sensor

https://github.com/RagnarEggertsson/MaintOptimization.git

• Proactive Service Logistics for Advanced Capital Goods Next, ProSeLoNext (grant code 438-15-620) [more info...] Netherlands Organization for Scientific Research; Dutch Institute for Advanced Logistics
• PrimaVera (grant code NWA.1160.18.238) [more info...] NWO

TU Eindhoven, Department of Industrial Engineering & Innovation Sciences
Vrije Universiteit Amsterdam, Department of Operations Analytics
University of Pittsburgh, Department of Industrial Engineering