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An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal

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Abstract

This paper proposes a deep learning network (DLN) as the basis for a bearing fault diagnosis technique, which is constructed by the autoencoders and softmax classifier for the purpose of identifying the various multi-degree bearing fault. Firstly, the ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into intrinsic mode functions (IMFs). A high-dimensionality feature vector is formed by analyzing the statistical parameters in the time domain and the frequency domain of the first several IMFs. Then, this feature vector serves as the input for DLN to classify the bearing fault pattern. In a DLN, an autoencoder performs the unsupervised feature self-learning phase to generate a final significant feature vector for training the softmax classifier. Finally, the parameters of a complete DLN based on stacking auto-encoders and the softmax classifier together is fine-tuned with respect to supervised learning criterion aiming to optimize the classification error. Experimental results have shown a great effect for bearing fault diagnosis based on the proposed DLN. The identification accuracy result has been achieved in the bearing fault status even with the unpredictable defects tests on the inner race and roller element of bearing. Methodologies in this study offer confidence for complex data classification.

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Authors and Affiliations

  1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China

    V. Hung Nguyen, J. Sheng Cheng, Yang Yu & V. Trong Thai

  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China

    V. Hung Nguyen, J. Sheng Cheng, Yang Yu & V. Trong Thai

  3. Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, 100000, Viet Nam

    V. Hung Nguyen & V. Trong Thai

Authors
  1. V. Hung Nguyen
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  2. J. Sheng Cheng
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  3. Yang Yu
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  4. V. Trong Thai
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Corresponding authors

Correspondence to V. Hung Nguyen or J. Sheng Cheng.

Additional information

Recommended by Associate Editor Sungsoo Na

V. Hung Nguyen is a doctor of Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam. He received his doctor degree in mechanical engineering from Hunan University, China.

J. Sheng Cheng is a Professor at Hunan University, Changsha, China. He works at the College of Mechanical and Vehicle Engineering and State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University. His research interests are the signal processing techniques, machine learning methods, fault identification and health monitoring of mechanical engineering systems.

Yang Yu is a Professorof the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China. She works also at State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body. Her research interests are the measurement, fault diagnosis, signal processing, and health monitoring of mechanical systems.

V. Trong Thai received M.S. degree in Mechanical Engineering from Hanoi University of Science and Technology, Hanoi, Vietnam, in 2011. He works at Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam. Currently, He researches on fault diagnosis methods for achieving Ph.D. degree at Hunan University, Changsha, China.

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Nguyen, V.H., Cheng, J.S., Yu, Y. et al. An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal. J Mech Sci Technol 33, 41–50 (2019). https://doi.org/10.1007/s12206-018-1205-6

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  • DOI: https://doi.org/10.1007/s12206-018-1205-6

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