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Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor

Author

Listed:
  • Jialin Li

    (Northeastern University)

  • Xueyi Li

    (Northeastern University)

  • David He

    (University of Illinois at Chicago)

  • Yongzhi Qu

    (University of Minnesota Duluth)

Abstract

In recent years, deep learning based diagnostic approaches have become more attractive. However, most of these methods are supervised diagnostic approaches. Developing a supervised diagnostic model requires a large number of labeled training data. And it is time consuming and labor intensive to obtain labeled data for a variety of systems and working conditions. Therefore, an unsupervised diagnostic model that does not require labeled training data is more desirable. This paper proposes an unsupervised diagnostic model by integrating a sparse autoencoder, a deep belief network, and a binary processor. In comparison with the existing unsupervised methods, the proposed method does not need to perform statistical features extraction, and directly uses the normalized frequency domain signals as the inputs. Moreover, in the proposed diagnostic model, the input data is passed through layer by layer without fine-tuning, which is completely unsupervised process. The proposed methods have been validated with bearing fault datasets and gear pitting fault datasets. The validation results show that the proposed method has a higher accuracy for both bearing and gear pitting fault diagnosis.

Suggested Citation

  • Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01543-8
    DOI: 10.1007/s10845-020-01543-8
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    References listed on IDEAS

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    1. Ridha Ziani & Ahmed Felkaoui & Rabah Zegadi, 2017. "Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 405-417, February.
    2. Cong Wang & Meng Gan & Chang’an Zhu, 2019. "A supervised sparsity-based wavelet feature for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 229-239, January.
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
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    Citations

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    Cited by:

    1. Jiyoung Song & Young Chul Lee & Jeongsu Lee, 2023. "Deep generative model with time series-image encoding for manufacturing fault detection in die casting process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3001-3014, October.
    2. Jinyang Jiao & Ming Zhao & Jing Lin & Kaixuan Liang & Chuancang Ding, 2022. "A mixed adversarial adaptation network for intelligent fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2207-2222, December.
    3. Dechen Yao & Hengchang Liu & Jianwei Yang & Jiao Zhang, 2021. "Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 729-743, March.
    4. Dionísio H. C. S. S. Martins & Amaro A. Lima & Milena F. Pinto & Douglas de O. Hemerly & Thiago de M. Prego & Fabrício L. e Silva & Luís Tarrataca & Ulisses A. Monteiro & Ricardo H. R. Gutiérrez & Die, 2023. "Hybrid data augmentation method for combined failure recognition in rotating machines," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1795-1813, April.
    5. Xiaoyin Nie & Gang Xie, 2021. "A novel normalized recurrent neural network for fault diagnosis with noisy labels," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1271-1288, June.

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