End-to-end continuous/discontinuous feature fusion method with attention for rolling bearing fault diagnosis

HIGHLIGHTS

• who: Jianbo Zheng et al. from the Institute of Vibration and Noise, Naval University of Engineering, Wuhan, China have published the research work: End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis, in the Journal: Sensors 2022, 6489 of /2022/
• what: The authors compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. This work has the following contributions: 1. This work proposes a context-dependent attention module for LSTM. The authors proposed . . .
  

   

  

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