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A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

Author

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  • Jialin Li
  • Xueyi Li
  • David He
  • Yongzhi Qu

Abstract

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

Suggested Citation

  • Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network," Journal of Risk and Reliability, , vol. 234(1), pages 168-182, February.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:1:p:168-182
    DOI: 10.1177/1748006X19867776
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    References listed on IDEAS

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    1. 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.
    2. 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. Chen Yin & Yulin Wang & Yan He & Lu Liu & Yan Wang & Guannan Yue, 2021. "Early fault diagnosis of ball screws based on 1-D convolution neural network and orthogonal design," Journal of Risk and Reliability, , vol. 235(5), pages 783-797, October.
    2. Jamil, Faras & Verstraeten, Timothy & Nowé, Ann & Peeters, Cédric & Helsen, Jan, 2022. "A deep boosted transfer learning method for wind turbine gearbox fault detection," Renewable Energy, Elsevier, vol. 197(C), pages 331-341.

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