Improving Total Productive Maintenance through Machine Learning for failure prediction - Oliveira, Gabriel Sotello de; Luche, José Roberto Dale; Silva, Aneirson Francisco da; Fernandes, Leandro Carlos; Freitas, Claudia Regina de (Brazilian Journal of Production Engineering, 2026)
When treated correctly, data can generate valuable information. Technological advancement has contributed to the development of tools for analyzing large volumes of data, along with algorithms capable of identifying patterns and making predictions based on statistical methods. In this study, ways of applying data analysis and methods based on machine learning are investigated using a synthetic database ’AI4I’ for engine failures. The method used in this work consisted of going through all the stages of applying machine learning, from data selection, treatment, visualizations, and the creation of five predictive models using different algorithms and their interpretations. Python was used as the programming language. Finally, it was possible to compare the performance of the different algorithms and conclude that the Random Forest method proved to be more efficient, with an accuracy of almost 100%, indicating that this model is effective for the specific database.