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Data-enabled Bayesian inference for strategic maintenance decisions in industrial operations.

Raúl Torres-Sainz1, Leandro L Lorente-Leyva2,3, Yorley Arbella-Feliciano1

  • 1CAD/CAM Study Center, University of Holguín, Holguín, Cuba.

Data in Brief
|November 18, 2024
PubMed
Summary

This study introduces a new dataset for selecting industrial maintenance strategies. The data, generated via Monte Carlo simulations, aids in developing data-driven decision-making models for equipment management.

Keywords:
Intelligent predictive maintenanceMaintenance managementMaintenance strategy selectionMonte Carlo simulation

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Asset Management

Background:

  • Effective industrial asset and equipment management relies on optimal maintenance strategy selection.
  • Current methods may lack comprehensive data for evaluating diverse maintenance scenarios.
  • Data-driven approaches are increasingly vital for optimizing industrial operations.

Purpose of the Study:

  • To present a novel dataset for evaluating 12 key criteria in maintenance strategy selection.
  • To facilitate the development of advanced models for industrial maintenance decision-making.
  • To support reproducibility and further research in data-driven maintenance.

Main Methods:

  • Monte Carlo simulations were employed to generate a comprehensive dataset.
  • The dataset encompasses a wide range of potential industrial maintenance scenarios.
  • Data normalization and structuring were performed for ease of analysis and modeling.

Main Results:

  • A dataset evaluating 12 critical criteria for maintenance strategy selection has been generated.
  • The data covers diverse maintenance scenarios, suitable for various industrial contexts.
  • The dataset is structured for direct application in further modeling and analysis.

Conclusions:

  • The provided dataset supports the development of new models for maintenance strategy selection.
  • It encourages the adoption of data-driven approaches in industrial maintenance.
  • The dataset serves as a valuable resource for research, education, and practical application in maintenance operations.