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Publisher's Note: "Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method" [Rev. Sci. Instrum. 95, 055104 (2024)].

The Review of scientific instruments·2024
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An adaptive fully convolutional network for bearing fault diagnosis under noisy environments.

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Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the

Xinliang Zhang1, Yanqi Wang1,2, Yitian Zhou3

  • 1School of Electrical Engineering and Automation, Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Henan Polytechnic University, Jiaozuo 454003, China.

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This study introduces a coupled adversarial autoencoder (CoAAE) to generate synthetic data for deep learning fault diagnosis. The method effectively augments imbalanced datasets, improving diagnostic model accuracy and stability.

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Area of Science:

  • Engineering
  • Computer Science
  • Machine Learning

Background:

  • Deep learning fault diagnosis requires extensive labeled data.
  • Insufficient or imbalanced samples degrade model performance and stability.

Purpose of the Study:

  • To develop a novel data augmentation method for deep learning fault diagnosis.
  • To address challenges of small-sized and imbalanced datasets.

Main Methods:

  • Introduced a coupled adversarial autoencoder (CoAAE) utilizing Bayesian methods.
  • CoAAE generates synthetic samples by capturing data distribution and adversarial training.
  • A parallel coupled network addresses sample imbalance by learning joint distributions.

Main Results:

  • CoAAE effectively augments imbalanced datasets for fault diagnosis.
  • Experiments on a bearing dataset demonstrated superior performance over advanced methods.
  • The method improved accuracy and stability of deep learning diagnosis models.

Conclusions:

  • The proposed CoAAE method offers a robust solution for data augmentation in fault diagnosis.
  • This approach enhances the reliability and accuracy of deep learning diagnostic models with limited data.