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MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network.

Feiqing Zhang1,2,3, Zhenyu Yin1,2,3, Fulong Xu1,2,3

  • 1Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.

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PubMed
Summary

This study introduces MAB-DrNet, a novel deep learning method for diagnosing rolling bearing faults in noisy conditions. The method achieves high accuracy, demonstrating robust noise immunity for reliable equipment operation.

Keywords:
deep learningdilated convolutionfault diagnosisnoisy environmentresidual network

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Rolling bearing fault diagnosis is critical for manufacturing equipment safety.
  • Noisy environments degrade the performance of existing deep learning fault diagnosis methods.
  • Nonlinear characteristics in bearing signals complicate accurate fault detection.

Purpose of the Study:

  • To propose an improved deep learning model for bearing fault diagnosis in noisy environments.
  • To enhance feature extraction and global information handling capabilities for better accuracy.
  • To develop a noise-resilient method for reliable fault identification.

Main Methods:

  • Designed a dilated residual network (DrNet) to expand the perceptual field for feature capture.
  • Developed a max-average block (MAB) module to enhance feature extraction.
  • Integrated a global residual block (GRB) module to improve global information processing.

Main Results:

  • The proposed MAB-DrNet demonstrated strong noise immunity on the CWRU dataset.
  • Achieved 95.57% accuracy with added Gaussian white noise at a -6 dB SNR.
  • Outperformed existing advanced methods in noisy bearing fault diagnosis.

Conclusions:

  • MAB-DrNet offers a highly accurate and noise-resilient solution for rolling bearing fault diagnosis.
  • The model effectively handles complex noisy environments, improving equipment reliability.
  • The integration of MAB and GRB modules significantly enhances diagnostic performance.