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Comparisons of different deep learning-based methods on fault diagnosis for geared system

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  • Bing Han
  • Xiaohui Yang
  • Yafeng Ren
  • Wanggui Lan

Abstract

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

Suggested Citation

  • Bing Han & Xiaohui Yang & Yafeng Ren & Wanggui Lan, 2019. "Comparisons of different deep learning-based methods on fault diagnosis for geared system," International Journal of Distributed Sensor Networks, , vol. 15(11), pages 15501477198, November.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:11:p:1550147719888169
    DOI: 10.1177/1550147719888169
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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    Cited by:

    1. Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.

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