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Machine learning for predictive maintenance scheduling of distribution transformers

Laura Isabel Alvarez Quiñones (School of Electrical and Electronic Engineering, Universidad del Valle, Cali, Colombia)
Carlos Arturo Lozano-Moncada (School of Electrical and Electronic Engineering, Universidad del Valle, Cali, Colombia)
Diego Alberto Bravo Montenegro (Physics Department, Universidad del Cauca, Popayán, Colombia)

Journal of Quality in Maintenance Engineering

ISSN: 1355-2511

Article publication date: 24 January 2022

Issue publication date: 7 March 2023

709

Abstract

Purpose

The purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning.

Design/methodology/approach

The proposed methodology relies on classification predictive model that finds the minimal number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in Cauca Department Colombia.

Findings

The implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.

Originality/value

The proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.

Keywords

Acknowledgements

The authors would like to recognize and express their sincere gratitude to Compañia Energética de Occidente, Universidad del Valle and Universidad del Cauca, (Colombia) for the academic support granted during this project.

Citation

Alvarez Quiñones, L.I., Lozano-Moncada, C.A. and Bravo Montenegro, D.A. (2023), "Machine learning for predictive maintenance scheduling of distribution transformers", Journal of Quality in Maintenance Engineering, Vol. 29 No. 1, pp. 188-202. https://doi.org/10.1108/JQME-06-2021-0052

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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