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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

Author

Listed:
  • Seif Eddine Chehaidia

    (Industrial Mechanics Laboratory, Badji Mokhtar Annaba University, Box 12, Annaba 23000, Algeria)

  • Hakima Cherif

    (LGEB Laboratory, Department of Electrical Engineering, Biskra University, Biskra 07000, Algeria)

  • Musfer Alraddadi

    (Yanbu Industrial College (YIC), Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Mohamed Ibrahim Mosaad

    (Yanbu Industrial College (YIC), Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Abdelaziz Mahmoud Bouchelaghem

    (Industrial Mechanics Laboratory, Badji Mokhtar Annaba University, Box 12, Annaba 23000, Algeria)

Abstract

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6746-:d:915825
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    References listed on IDEAS

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    1. 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.
    2. Mikko Tahkola & Áron Szücs & Jari Halme & Akhtar Zeb & Janne Keränen, 2022. "A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study," Energies, MDPI, vol. 15(9), pages 1-23, May.
    3. Zuolu Wang & Jie Yang & Haiyang Li & Dong Zhen & Yuandong Xu & Fengshou Gu, 2019. "Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis," Energies, MDPI, vol. 12(17), pages 1-20, August.
    4. Cherif, Hakima & Benakcha, Abdelhamid & Laib, Ismail & Chehaidia, Seif Eddine & Menacer, Arezky & Soudan, Bassel & Olabi, A.G., 2020. "Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor," Energy, Elsevier, vol. 212(C).
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    Cited by:

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