IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i4d10.1007_s10845-021-01904-x.html
   My bibliography  Save this article

Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks

Author

Listed:
  • Cuixia Jiang

    (Hefei University of Technology)

  • Hao Chen

    (Hefei University of Technology)

  • Qifa Xu

    (Hefei University of Technology
    Ministry of Education)

  • Xiangxiang Wang

    (Anhui Ronds Science & Technology Incorporated Company)

Abstract

In the fault diagnosis of rotating machinery, vibration signal or spectrum is usually used. As a data-driven method, deep learning has been introduced into the field of fault diagnosis. But it often confronts with two critical difficulties: few fault cases and single data source. To this end, we employ the prototype network to solve the problem of few fault cases, and use the two-branch technique to combine data sources in time domain and frequency domain. We introduce the two-branch network structure into the framework of the prototype network and develop a two-branch prototype network (TBPN) for fault diagnosis. The TBPN model is constructed through three main steps. First, we extract information from vibration signals in time domain and frequency domain respectively, and feed them into the model as two branches. Second, the prototype representation of each fault in time domain and frequency domain can be learned through metric learners, and the distances between fault prototypes and query faults features are then calculated. Third, the distances in time domain and frequency domain are integrated and incorporated into the softmax function for multi-classification. The performance of TBNP is verified by a real-world application on fault diagnosis of rotating machinery with the case data accumulated by an industrial Internet enterprise in China. The results show that the TBPN model is suitable for fault diagnosis in the case of small data. Compared with using time domain signals or spectrum alone, their combination use can improve the effectiveness of fault diagnosis.

Suggested Citation

  • Cuixia Jiang & Hao Chen & Qifa Xu & Xiangxiang Wang, 2023. "Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1667-1681, April.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01904-x
    DOI: 10.1007/s10845-021-01904-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01904-x
    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/s10845-021-01904-x?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. Qifa Xu & Shixiang Lu & Weiyin Jia & Cuixia Jiang, 2020. "Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1467-1481, August.
    2. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    3. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    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. Li, Guolong & Li, Yanjun & Fang, Chengyue & Su, Jian & Wang, Haotong & Sun, Shengdi & Zhang, Guolei & Shi, Jianxin, 2023. "Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning," Energy, Elsevier, vol. 281(C).

    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. 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.
    2. Andhi Indira Kusuma & Yi-Mei Huang, 2023. "Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1683-1699, April.
    3. Wang, Zixuan & Qin, Bo & Sun, Haiyue & Zhang, Jian & Butala, Mark D. & Demartino, Cristoforo & Peng, Peng & Wang, Hongwei, 2023. "An imbalanced semi-supervised wind turbine blade icing detection method based on contrastive learning," Renewable Energy, Elsevier, vol. 212(C), pages 251-262.
    4. 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.
    5. Rui Zhang & Na Zhao & Liuhu Fu & Xiaolu Bai & Jianghui Cai, 2023. "Recognizing defects in stainless steel welds based on multi-domain feature expression and self-optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1293-1309, March.
    6. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    7. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
    9. Xie, Tianming & Xu, Qifa & Jiang, Cuixia & Lu, Shixiang & Wang, Xiangxiang, 2023. "The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 202(C), pages 143-153.
    10. 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.
    11. Chuanxia Jian & Yinhui Ao, 2023. "Imbalanced fault diagnosis based on semi-supervised ensemble learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3143-3158, October.
    12. Jin, Zhenglei & Xu, Qifa & Jiang, Cuixia & Wang, Xiangxiang & Chen, Hao, 2023. "Ordinal few-shot learning with applications to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 206(C), pages 1158-1169.
    13. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
    14. Jiangming Jia & Chenan Zhang & Jianneng Chen & Zheng Zhu & Ming Mao, 2022. "Fault Diagnosis Analysis of Angle Grinder Based on ACD-DE and SVM Hybrid Algorithm," Mathematics, MDPI, vol. 10(18), pages 1-16, September.
    15. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    16. 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.

    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:34:y:2023:i:4:d:10.1007_s10845-021-01904-x. 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.