Machine learning for predictive maintenance scheduling of distribution transformers

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.