Data-enabled Bayesian inference for strategic maintenance decisions in industrial operations
Journal
Data in Brief
ISSN
2352-3409
Date Issued
2024-12
Author(s)
Raúl Torres-Sainz
Leandro L. Lorente-Leyva
Yorley Arbella-Feliciano
Carlos Alberto Trinchet-Varela
Lidia María Pérez-Vallejo
Roberto Pérez-Rodríguez
Abstract
Efficient management of industrial assets and equipment depends heavily on the selection of appropriate maintenance strategies.
This research presents a dataset generated through Monte Carlo simulations to evaluate 12 key criteria relevant to maintenance strategy selection.
The dataset covers a wide range of potential maintenance scenarios, providing comprehensive data for researchers to explore various strategies in industrial settings. The data were normalized and structured in a way that facilitates their use for further modeling or analysis.
The dataset offers an opportunity for researchers to reproduce the data collection process, enabling comparisons with their own studies.
By providing this dataset, we aim to support the development of new models for maintenance strategy selection and encourage further exploration of data-driven approaches in industrial maintenance.
Additionally, the dataset can serve educational purposes, assisting in the teaching of decision-making in the context of maintenance operations.
This research presents a dataset generated through Monte Carlo simulations to evaluate 12 key criteria relevant to maintenance strategy selection.
The dataset covers a wide range of potential maintenance scenarios, providing comprehensive data for researchers to explore various strategies in industrial settings. The data were normalized and structured in a way that facilitates their use for further modeling or analysis.
The dataset offers an opportunity for researchers to reproduce the data collection process, enabling comparisons with their own studies.
By providing this dataset, we aim to support the development of new models for maintenance strategy selection and encourage further exploration of data-driven approaches in industrial maintenance.
Additionally, the dataset can serve educational purposes, assisting in the teaching of decision-making in the context of maintenance operations.