Bearing fault diagnosis using machine learning and a novel set of fault-related spectral features

  • João Paulo Vieira UFSC
  • Rodrigo Kobashikawa Rosa UFSC
  • Victor Afonso Bauler UFSC
  • Danilo Braga Dynamox
  • Danilo Silva UFSC

Resumo


Este artigo desenvolve métodos baseados em aprendizado de máquina para diagnóstico de falhas em rolamentos com dados da Case Western Reserve University (CWRU). Adota-se uma abordagem multirrótulo proposta na literatura, com três classificadores binários para identificar falhas na trilha interna, externa e no elemento rolante, dividindo o conjunto de dados por rolamentos distintos, visando eliminar vazamento. Utilizam-se features no domínio do tempo e frequência, incluindo uma nova proposta de features espectrais, denominadas PFRs (Peak-To-Floor Ratios), calculadas sobre frequências ligadas ao fenômeno da falha. Os resultados, comparados com outras features da literatura, mostram melhorias no desempenho e explicabilidade.
Palavras-chave: Falha em rolamentos, Vazamento de dados, Atributos espectrais

Referências

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Publicado
17/11/2024
VIEIRA, João Paulo; ROSA, Rodrigo Kobashikawa; BAULER, Victor Afonso; BRAGA, Danilo; SILVA, Danilo. Bearing fault diagnosis using machine learning and a novel set of fault-related spectral features. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 120-131. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2024.245033.

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