IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3834-d390105.html
   My bibliography  Save this article

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

Author

Listed:
  • Zia Ullah

    (Department of Electrical Engineering, Incheon National University, Incheon 22012, Korea)

  • Bilal Ahmad Lodhi

    (Department of Computer Engineering, Queen’s University, Belfast Bt7 1nn, UK)

  • Jin Hur

    (Department of Electrical Engineering, Incheon National University, Incheon 22012, Korea)

Abstract

Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques.

Suggested Citation

  • Zia Ullah & Bilal Ahmad Lodhi & Jin Hur, 2020. "Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG," Energies, MDPI, vol. 13(15), pages 1-17, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3834-:d:390105
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3834/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/15/3834/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zia Ullah & Jin Hur, 2018. "A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines," Energies, MDPI, vol. 11(12), pages 1-27, November.
    2. Chen Lu & Yang Wang & Minvydas Ragulskis & Yujie Cheng, 2016. "Fault Diagnosis for Rotating Machinery: A Method based on Image Processing," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-22, October.
    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. Yinquan Yu & Pan Zhao & Yong Hao & Dequan Zeng & Yiming Hu & Bo Zhang & Hui Yang, 2022. "Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm," Energies, MDPI, vol. 16(1), pages 1-11, December.
    2. Pawel Ewert & Teresa Orlowska-Kowalska & Kamila Jankowska, 2021. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks," Energies, MDPI, vol. 14(3), pages 1-24, January.
    3. Xiaohua Song & Jing Liu & Chaobo Chen & Song Gao, 2022. "Advanced Methods in Rotating Machines," Energies, MDPI, vol. 15(15), pages 1-3, July.

    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. Mariusz Korkosz & Jan Prokop & Bartlomiej Pakla & Grzegorz Podskarbi & Piotr Bogusz, 2020. "Analysis of Open-Circuit Fault in Fault-Tolerant BLDC Motors with Different Winding Configurations," Energies, MDPI, vol. 13(20), pages 1-27, October.
    2. Piotr Mynarek & Janusz Kołodziej & Adrian Młot & Marcin Kowol & Marian Łukaniszyn, 2021. "Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding," Energies, MDPI, vol. 14(16), pages 1-16, August.
    3. Ahmed Belkhadir & Remus Pusca & Driss Belkhayat & Raphaël Romary & Youssef Zidani, 2023. "Analytical Modeling, Analysis and Diagnosis of External Rotor PMSM with Stator Winding Unbalance Fault," Energies, MDPI, vol. 16(7), pages 1-23, April.
    4. Chunming Wu & Zhou Zeng, 2021. "A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
    5. Syidy Ab Rasid & Konstantinos N. Gyftakis & Markus Mueller, 2023. "Comparative Investigation of Three Diagnostic Methods Applied to Direct-Drive Permanent Magnet Machines Suffering from Demagnetization," Energies, MDPI, vol. 16(6), pages 1-18, March.
    6. Haiping Li & Jianmin Zhao & Xianglong Ni & Xinghui Zhang, 2018. "Fault diagnosis for machinery based on feature extraction and general regression neural network," 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. 9(5), pages 1034-1046, October.
    7. Milan Oravec & Pavol Lipovský & Miroslav Šmelko & Pavel Adamčík & Mirosław Witoś & Jerzy Kwaśniewski, 2021. "Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    8. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Dinesh V. Thangamuthu & Antoni Garcia, 2020. "Detection of Partial Demagnetization Faults in Five-Phase Permanent Magnet Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 13(13), pages 1-17, July.
    9. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.

    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:13:y:2020:i:15:p:3834-:d:390105. 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.