IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01543-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01543-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01543-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    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. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    2. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    3. Swapnil K. Gundewar & Prasad V. Kane, 2022. "Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2876-2894, December.
    4. Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    6. Jinhai Chen & Wenyuan Zhang & Heng Wang, 2021. "Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 745-756, March.
    7. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    8. Wangpeng He & Peipei Zhang & Xuan Liu & Binqiang Chen & Baolong Guo, 2022. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    9. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    10. Dengyu Xiao & Chengjin Qin & Honggan Yu & Yixiang Huang & Chengliang Liu, 2021. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 377-391, February.
    11. Gang Wang & Feng Zhang & Bayi Cheng & Fang Fang, 2021. "DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 1-20, January.
    12. Amine Mezaghcha & Ridha Ziani & Ahmed Felkaoui, 2023. "Empirical wavelet decomposition and BFindex for early detection of bearing defects," Journal of Risk and Reliability, , vol. 237(6), pages 1223-1233, December.
    13. 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.
    14. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01543-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.