Machine Learning Algorithms for Remaining Useful Life Prediction of Rolling Bearings

  • Débora Zumpichiatti UFRJ
  • Janaína Gomide UFRJ

Resumo


O aumento da complexidade dos sistemas mecânicos muda drasticamente os métodos usados para monitorar e analisar como esses sistemas envelhecem. O objetivo desse trabalho é realizar a previsão do tempo de vida útil restante de equipamentos utilizando uma abordagem de prognóstico baseada em dados e algoritmos de aprendizado de máquina. O conjunto de dados utilizado apresenta dados de temperatura e vibração de testes até a falha de rolamentos. A metodologia proposta foi avaliada e constatou-se a importância de uma fase de tratamento de dados robusta. Os resultados obtidos para conjuntos de dados julgados como apropriados pela metodologia apresentaram resultados similares ou superiores aos trabalhos relacionados.

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Publicado
28/11/2022
ZUMPICHIATTI, Débora; GOMIDE, Janaína. Machine Learning Algorithms for Remaining Useful Life Prediction of Rolling Bearings. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 258-269. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2022.227195.