MIXCNN, TF2

A fault diagnosis method for rotating machinery based on CNN with mixed information

This paper proposes the CNN with mixed information (MIXCNN), a classification model that is efficient and lightweight for end-to-end fault diagnosis, and the convolutional layers maintain the same size of the output throughout the network. The MIXCNN uses depthwise convolution to increase the ability of discrimination in spatial locations and uses traditional convolution to achieve cross-channel interaction of information; then, the residual connection is introduced to reduce the loss of information on the convolutional layers.

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@ARTICLE{9964316,
  author={Zhao, Zhiqian and Jiao, Yinghou},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={A Fault Diagnosis Method for Rotating Machinery Based on CNN With Mixed Information}, 
  year={2023},
  volume={19},
  number={8},
  pages={9091-9101},
  doi={10.1109/TII.2022.3224979}}