Machine learning for predictive maintenance scheduling of distribution transformers

• Published in: Journal of quality in maintenance engineering Vol. 29; no. 1; pp. 188 - 202
• Main Authors: Alvarez Quiñones, Laura Isabel, Lozano-Moncada, Carlos Arturo, Bravo Montenegro, Diego Alberto
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 07.03.2023; Emerald Group Publishing Limited
• Subjects: Algorithms; Artificial intelligence; Classification; Cost reduction; Datasets; Decision making; Electric power; Electricity; Electricity distribution; Linear programming; Machine learning; Maintenance management; Mathematical models; Methodology; Neural networks; Optimization techniques; Power supply; Prediction models; Predictive maintenance; Preventive maintenance; Quality of service; Scheduling; Transformers; Variables; Scheduling; Distribution transformers; Machine learning; Maintenance planning; Predictive maintenance
• ISSN: 1355-2511; 1758-7832
• DOI: 10.1108/JQME-06-2021-0052

PurposeThe 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/approachThe 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.FindingsThe implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.Originality/valueThe proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.