most citedTowards a more realistic evaluation of machine learning models for bearing fault diagnosis

3 citations · 5 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2025★ 3 cited

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

João Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa +1

Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery. While recent advances in machine learning (ML), parti…

eess.SP2024★ 2 cited

Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach

Rodrigo Kobashikawa Rosa, Danilo Braga, Danilo Silva

This paper proposes a novel approach for modeling the problem of fault diagnosis using the Case Western Reserve University (CWRU) bearing fault dataset. Although the dataset is con…