Torres-Sainz Raúl, Lorente-Leyva Leandro L, Arbella-Feliciano Yorley, Trinchet-Varela Carlos Alberto, Pérez-Vallejo Lidia María, Pérez-Rodríguez Roberto
CAD/CAM Study Center, University of Holguín, Holguín, Cuba.
SDAS Research Group (sdas-group.com/), Ben Guerir, 43150, Morocco.
Data Brief. 2024 Oct 22;57:111058. doi: 10.1016/j.dib.2024.111058. eCollection 2024 Dec.
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.
工业资产和设备的有效管理在很大程度上取决于合适维护策略的选择。本研究呈现了一个通过蒙特卡洛模拟生成的数据集，用于评估与维护策略选择相关的12个关键标准。该数据集涵盖了广泛的潜在维护场景，为研究人员在工业环境中探索各种策略提供了全面的数据。数据经过归一化处理并以便于进一步建模或分析的方式进行了结构化。该数据集为研究人员提供了重现数据收集过程的机会，使其能够与自己的研究进行比较。通过提供这个数据集，我们旨在支持维护策略选择新模型的开发，并鼓励在工业维护中进一步探索数据驱动的方法。此外，该数据集可用于教育目的，辅助在维护操作背景下的决策教学。
