IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2151-d1077582.html
   My bibliography  Save this article

Research on Transformer Voiceprint Anomaly Detection Based on Data-Driven

Author

Listed:
  • Da Yu

    (School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Wei Zhang

    (School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Hui Wang

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China)

Abstract

Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for abnormal diagnosis of power transformers based on the Attention-CNN-LSTM hybrid model is proposed. This collects the sound signals emitted by the real power transformer in the normal state, overload, and the discharge condition. It preprocesses the sound signals to obtain the MFCC characteristics of the sound signals. It is then grouped into a set of sound feature vectors by the first- and second-order differences, and enters the Attention-CNN-LSTM hybrid model for training. The training results show that the Attention-CNN-LSTM hybrid model can be used for the status sound detection of power transformers, and the recognition of the three states can achieve an accuracy rate of more than 99%.

Suggested Citation

  • Da Yu & Wei Zhang & Hui Wang, 2023. "Research on Transformer Voiceprint Anomaly Detection Based on Data-Driven," Energies, MDPI, vol. 16(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2151-:d:1077582
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2151/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2151/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:16:y:2023:i:5:p:2151-:d:1077582. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.