IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5551-d1200173.html
   My bibliography  Save this article

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

Author

Listed:
  • Reza Bazghandi

    (Department of Electrical Engineering and Robotic, Shahrood University of Technology, Shahrood 36199-95161, Iran)

  • Mohammad Hoseintabar Marzebali

    (Department of Electrical Engineering and Robotic, Shahrood University of Technology, Shahrood 36199-95161, Iran)

  • Vahid Abolghasemi

    (School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK)

  • Shahin Hedayati Kia

    (Laboratory MIS UR4290, University of Picardie “Jules Verne”, 33 rue St Leu, 80039 Amiens, France)

Abstract

Condition monitoring of induction machines (IMs) with the aim of increasing the machine’s lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions.

Suggested Citation

  • Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5551-:d:1200173
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5551/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/14/5551/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vicente Biot-Monterde & Ángela Navarro-Navarro & Jose A. Antonino-Daviu & Hubert Razik, 2021. "Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters," Energies, MDPI, vol. 14(18), pages 1-18, September.
    2. Yan Zhao & Haohan Cui & Hong Huo & Yonghui Nie, 2018. "Application of Synchrosqueezed Wavelet Transforms for Extraction of the Oscillatory Parameters of Subsynchronous Oscillation in Power Systems," Energies, MDPI, vol. 11(6), pages 1-18, June.
    3. Rahul R. Kumar & Mauro Andriollo & Giansalvo Cirrincione & Maurizio Cirrincione & Andrea Tortella, 2022. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors," Energies, MDPI, vol. 15(23), pages 1-36, November.
    4. Sudip Halder & Sunil Bhat & Daria Zychma & Pawel Sowa, 2022. "Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review," Energies, MDPI, vol. 15(22), pages 1-20, November.
    5. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Ming Ye & Jian Zhang & Jiaqiang Yang, 2022. "Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis," Energies, MDPI, vol. 15(10), pages 1-16, May.
    7. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    8. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    9. Moritz Benninger & Marcus Liebschner & Christian Kreischer, 2023. "Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework," Energies, MDPI, vol. 16(8), pages 1-20, April.
    10. Hagar A. Ali & Moataz M. Elsherbini & Mohamed I. Ibrahem, 2022. "Wavelet Transform Processor Based Surface Acoustic Wave Devices," Energies, MDPI, vol. 15(23), pages 1-18, November.
    11. Angela Navarro-Navarro & Israel Zamudio-Ramirez & Vicente Biot-Monterde & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors," Energies, MDPI, vol. 15(7), pages 1-19, March.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Chao Huang & Siqi Bu & Hiu Hung Lee & Kwong Wah Chan & Winco K. C. Yung, 2024. "Prognostics and health management for induction machines: a comprehensive review," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 937-962, March.
    3. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    4. Yue, Ke & Li, Jipu & Deng, Shuhan & Kwoh, Chee Keong & Chen, Zhuyun & Li, Weihua, 2024. "A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    5. Vasilev Bogdan Yurievich & Nguyen The Hien, 2024. "Stochastic Pulse-Width Modulation and Modification of Direct Torque Control Based on a Three-Level Neutral-Point Clamped Inverter," Energies, MDPI, vol. 17(23), pages 1-24, November.
    6. Boris V. Malozyomov & Nikita V. Martyushev & Nikita V. Babyr & Alexander V. Pogrebnoy & Egor A. Efremenkov & Denis V. Valuev & Aleksandr E. Boltrushevich, 2024. "Modelling of Reliability Indicators of a Mining Plant," Mathematics, MDPI, vol. 12(18), pages 1-25, September.
    7. Ailton O. Louzada & Wesley A. Souza & Avyner L. O. Vitor & Marcelo F. Castoldi & Alessandro Goedtel, 2025. "Detection of Stator Faults in Three-Phase Induction Motors Using Stray Flux and Machine Learning," Energies, MDPI, vol. 18(6), pages 1-26, March.
    8. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. Jie Zhou & Zerui Xi & Shilong Wang & Bo Yang & Youhong Zhang & Yucheng Zhang, 2024. "A real spatial–temporal attention denoising network for nugget quality detection in resistance spot weld," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2743-2764, August.
    10. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Bon-Gwan Gu, 2022. "Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square," Energies, MDPI, vol. 15(22), pages 1-12, November.
    12. Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Pavel V. Shishkin & Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Development of a Mathematical Model of Operation Reliability of Mine Hoisting Plants," Mathematics, MDPI, vol. 12(12), pages 1-26, June.
    14. Lin, Yanzhuo & Wang, Yu & Zhang, Mingquan & Zhao, Ming, 2025. "A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    15. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.
    16. Wu, Zhangjun & Xu, Renli & Luo, Yuansheng & Shao, Haidong, 2024. "A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    17. Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
    18. Wan, Liangqi & Wei, Yumeng & Zhang, Qiaoke & Liu, Lei & Chen, Yuejian, 2025. "A new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    19. Dong, Manman & Cheng, Yongbo & Wan, Liangqi, 2024. "A new adaptive multi-kernel relevance vector regression for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Wentao Li & Qiankun Liu & Siyang Ye & Surong Huang, 2024. "Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques," Energies, MDPI, vol. 17(21), pages 1-30, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:14:p:5551-:d:1200173. 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.