Production
https://prod.org.br/article/doi/10.1590/0103-6513.20240010
Production
Research Article

Maintenance strategy selection using bayesian networks

Raúl Torres-Sainz; Leonardo Sánchez-Aguilera; Carlos Alberto Trinchet-Varela; Lidia María Pérez-Vallejo; Roberto Pérez-Rodríguez

http://dx.doi.org/10.1590/0103-6513.20240010 Production, vol.34, e20240010, 2024
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Abstract

Paper aims: The aim of this study is to develop a bayesian network model for selecting maintenance strategies.

Originality: This model evaluates the consequences and complexity of breakdowns, but also integrates the intelligent predictive maintenance policy, taking into account aspects such as available technology, diagnostic tools, staff training and the use of artificial intelligence, which can be applied at all levels.

Research method: A literature review was conducted to map the criteria used in the selection of maintenance strategies. The KANO model was then employed to select the criteria for the model, and a system of rules was established for the selection of maintenance strategies. The Monte Carlo method was used for the simulation of possible combinations in consideration of the rule system. Using this data, the bayesian network was learned, trained, and validated.

Main findings: The results show that the proposed model is highly reliable, with an accuracy of approximately 98%.

Implications for theory and practice: This study makes significant contributions to the field of maintenance by introducing a novel bayesian network model enriched with the Kano model. This comprehensive framework offers a practical approach for optimizing maintenance decisions by integrating both technical and perceptual aspects of maintenance.

Keywords

Maintenance management, Maintenance strategy selection, Bayesian networks

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Submitted date:
01/27/2024

Accepted date:
10/27/2024

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