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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Author

Listed:
  • Zhijian Wang
  • Likang Zheng
  • Wenhua Du
  • Wenan Cai
  • Jie Zhou
  • Jingtai Wang
  • Xiaofeng Han
  • Gaofeng He

Abstract

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

Suggested Citation

  • Zhijian Wang & Likang Zheng & Wenhua Du & Wenan Cai & Jie Zhou & Jingtai Wang & Xiaofeng Han & Gaofeng He, 2019. "A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network," Complexity, Hindawi, vol. 2019, pages 1-17, June.
    • Handle: RePEc:hin:complx:6943234
    DOI: 10.1155/2019/6943234
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    References listed on IDEAS

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    1. Zhijian Wang & Junyuan Wang & Wenan Cai & Jie Zhou & Wenhua Du & Jingtai Wang & Gaofeng He & Huihui He, 2019. "Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis," Complexity, Hindawi, vol. 2019, pages 1-17, May.
    2. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.
    3. Tata Subba Rao & Granville Tunnicliffe Wilson & Alessandro Cardinali & Guy P. Nason, 2017. "Locally Stationary Wavelet Packet Processes: Basis Selection and Model Fitting," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 151-174, March.
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    Cited by:

    1. Zhijian Wang & Likang Zheng & Junyuan Wang & Wenhua Du, 2019. "Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine," Complexity, Hindawi, vol. 2019, pages 1-19, November.
    2. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).

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