Context:

In this paper, we describe a methodology set up to schedule the predictive maintenance of distribution transformers in the Department of Cauca (Colombia) by means of machine learning.

Method:

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

Results:

It is possible to achieve an effective solution for scheduling the predictive maintenance of distribution transformers by means of machine learning.

Conclusions:

The proposed model is an effective tool for problems involving the scheduling of preventive maintenance scheduling problems for distribution transformers.

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