Machine Learning Algorithms for Remaining Useful Life Prediction of Rolling Bearings

• Débora Zumpichiatti UFRJ
• Janaína Gomide UFRJ

DOI: https://doi.org/10.5753/eniac.2022.227195

Abstract

The increasing complexity of mechanical systems changes the methods used to monitor and analyze how these systems age. The goal of this paper is to predict of the remaining useful life of equipments using a data-driven prognostic approach and machine learning algorithms. The dataset used presents temperature and vibration data from tests to run-to-failure of bearings. The proposed methodology was evaluated and the importance of a robust data processing phase was verified. The results obtained for data sets judged as appropriate by the methodology presented similar or superior results to the related works.

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