IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v238y2023ics0951832023003423.html
   My bibliography  Save this article

A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

Author

Listed:
  • Bai, Rui
  • Noman, Khandaker
  • Feng, Ke
  • Peng, Zhike
  • Li, Yongbo

Abstract

Simultaneous health monitoring and remaining useful life (RUL) prediction are important objectives in ensuring operational reliability and efficient maintenance of rolling bearings. However, most existing methods ignore the correlation between different degradation stages and RUL, and rarely study the uncertainty quantification of prediction. To overcome these issues, this paper proposes a two-phase-based deep neural network (TPDNN) method, which enables health monitoring and RUL prediction of bearings while providing uncertainty quantification. A logarithmic squared envelope-based diversity entropy is proposed to dynamically evaluate the health status of the bearings, and different degradation stages and RUL labels are adaptively established. Then the feedforward neural network is then used to achieve degradation stage (DS) identification in the first phase. The initial RUL prediction and two kinds of uncertainty quantification are implemented through the bayesian neural network in the second phase. Eventually, the correlation of the DS identification and RUL predictions is handled using a smoothing operator to obtain the final RUL. Experiments and comparisons on two bearing datasets verified that TPDNN has satisfactory prediction performance.

Suggested Citation

  • Bai, Rui & Noman, Khandaker & Feng, Ke & Peng, Zhike & Li, Yongbo, 2023. "A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
  • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003423
    DOI: 10.1016/j.ress.2023.109428
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023003423
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109428?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.

    Citations

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


    Cited by:

    1. Zhou, Zhihao & Zhang, Wei & Yao, Peng & Long, Zhenhua & Bai, Mingling & Liu, Jinfu & Yu, Daren, 2024. "More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    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:eee:reensy:v:238:y:2023:i:c:s0951832023003423. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.