A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Van-Cuong Nguyen, Duy-Tang Hoang, Xuan-Toa Tran, Mien Van, Hee-Jun Kang*

*Corresponding author for this work

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Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.
Original languageEnglish
Article numbere345
Issue number12
Early online date09 Dec 2021
Publication statusEarly online date - 09 Dec 2021


  • bearing fault diagnosis
  • deep learning
  • deep neural network


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