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Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition

Author

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  • Xinyue Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yan Yan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Kaibo Hu

    (Zhejiang Zheneng Lanxi Power Generation Co., Ltd., Jinhua 321199, China)

  • Shan Zhang

    (Zhejiang Zheneng Lanxi Power Generation Co., Ltd., Jinhua 321199, China)

  • Hongjie Li

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Zhen Zhang

    (Advanced Electrical Equipment Innovation Center, Zhejiang University, Hangzhou 311107, China)

  • Tingna Shi

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
    Advanced Electrical Equipment Innovation Center, Zhejiang University, Hangzhou 311107, China)

Abstract

When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency ( t - f ) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1196-:d:743448
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    References listed on IDEAS

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    1. Xiaobo Bi & Jiansheng Lin & Daijie Tang & Fengrong Bi & Xin Li & Xiao Yang & Teng Ma & Pengfei Shen, 2020. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals," Energies, MDPI, vol. 13(1), pages 1-20, January.
    2. Martin Valtierra-Rodriguez & Juan Pablo Amezquita-Sanchez & Arturo Garcia-Perez & David Camarena-Martinez, 2019. "Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors," Mathematics, MDPI, vol. 7(9), pages 1-19, August.
    3. Liling Sun & Boqiang Xu, 2018. "An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors," Energies, MDPI, vol. 11(11), pages 1-15, November.
    4. Abdenour Soualhi & Bilal El Yousfi & Hubert Razik & Tianzhen Wang, 2021. "A Novel Feature Extraction Method for the Condition Monitoring of Bearings," Energies, MDPI, vol. 14(8), pages 1-23, April.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    4. Luis Alonso Trujillo Guajardo & Miguel Angel Platas Garza & Johnny Rodríguez Maldonado & Mario Alberto González Vázquez & Luis Humberto Rodríguez Alfaro & Fernando Salinas Salinas, 2022. "Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors," Energies, MDPI, vol. 15(10), pages 1-24, May.
    5. Seif Eddine Chehaidia & Hakima Cherif & Musfer Alraddadi & Mohamed Ibrahim Mosaad & Abdelaziz Mahmoud Bouchelaghem, 2022. "Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(18), pages 1-22, September.

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