IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i17p3221-d259691.html
   My bibliography  Save this article

Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Author

Listed:
  • Yumin Hsueh

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Veeresha Ramesha Ittangihala

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Wei-Bin Wu

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Hong-Chan Chang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

  • Cheng-Chien Kuo

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan)

Abstract

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

Suggested Citation

  • Yumin Hsueh & Veeresha Ramesha Ittangihala & Wei-Bin Wu & Hong-Chan Chang & Cheng-Chien Kuo, 2019. "Condition Monitor System for Rotation Machine by CNN with Recurrence Plot," Energies, MDPI, vol. 12(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3221-:d:259691
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/17/3221/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/17/3221/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Ngaopitakkul & S. Bunjongjit, 2013. "An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(9), pages 1745-1761.
    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. Teen-Hang Meen & Wenbing Zhao & Cheng-Fu Yang, 2020. "Special Issue on Selected Papers from IEEE ICKII 2019," Energies, MDPI, vol. 13(8), pages 1-5, April.
    2. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
    3. Sarahi Aguayo-Tapia & Gerardo Avalos-Almazan & Jose de Jesus Rangel-Magdaleno & Juan Manuel Ramirez-Cortes, 2023. "Physical Variable Measurement Techniques for Fault Detection in Electric Motors," Energies, MDPI, vol. 16(12), pages 1-21, June.

    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. Junxun Chen & Longsheng Cheng & Hui Yu & Shaolin Hu, 2018. "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 147-159, January.

    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:12:y:2019:i:17:p:3221-:d:259691. 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: 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.