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Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform

Author

Listed:
  • Abderrahman El Idrissi

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Aziz Derouich

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Said Mahfoud

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Najib El Ouanjli

    (Laboratory of Mechanical, Computer, Electronics and Telecommunications, Faculty of Sciences and Technology, Hassan First University, Settat 26000, Morocco)

  • Ahmed Chantoufi

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Ameena Saad Al-Sumaiti

    (Advanced Power and Energy Center, Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Mahmoud A. Mossa

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

Abstract

Motor Current Signature Analysis (MCSA) is a popular method for the detection of faults in electric motor drives, particularly in Induction Machines (IMs). For Bearing Defects (BDs), which are very much related to the rotational frequency, it is important to maintain the speed at a target reference value in order to distinguish and locate the different BDs. This can be achieved by using a powerful control such as the Direct Torque Control (DTC), but this control causes the variation of the supply frequency and the current signal to become non-stationary, so the integration of advanced signal processing methods becomes necessary by using a suitable filter to handle the frequency content depending on the BDs, such as the Hilbert filter. This paper aims to adopt the Hilbert Transform (HT) for extracting the signature of the faults from the stator current envelope to detect the different BDs in the IMs when they are controlled by an intelligent DTC control driven by Artificial Neural Networks (ANN-DTC). This ANN-DTC control is a shaping factor rather than a disturbing one, which contributes with the Hilbert filter to the diagnosis of BDs. This technique is tested for the four locations of BDs: the inner ring, the outer ring, the ball, and the bearing cage in different operating situations without control and with conventional DTC and ANN-DTC controls. Thus, detecting the location of the defect exactly at an early stage contributes to achieving maintenance in a fairly short time. The performance of the chosen approach lies in minimizing the electromagnetic torque ripples as a result of the control and increase of the amplitudes of the spectra related to BDs compared to other harmonics. This performance is verified in the MATLAB/SIMULINK environment.

Suggested Citation

  • Abderrahman El Idrissi & Aziz Derouich & Said Mahfoud & Najib El Ouanjli & Ahmed Chantoufi & Ameena Saad Al-Sumaiti & Mahmoud A. Mossa, 2022. "Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform," Mathematics, MDPI, vol. 10(22), pages 1-32, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4258-:d:972343
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    References listed on IDEAS

    as
    1. Mahmoud A. Mossa & Hamdi Echeikh & Ahmed A. Zaki Diab & Hassan Haes Alhelou & Pierluigi Siano, 2021. "Comparative Study of Hysteresis Controller, Resonant Controller and Direct Torque Control of Five-Phase IM under Open-Phase Fault Operation," Energies, MDPI, vol. 14(5), pages 1-23, February.
    2. Tomas Zimnickas & Jonas Vanagas & Karolis Dambrauskas & Artūras Kalvaitis, 2020. "A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks," Energies, MDPI, vol. 13(14), pages 1-21, July.
    3. Hamdi Echeikh & Mahmoud A. Mossa & Nguyen Vu Quynh & Abdelsalam A. Ahmed & Hassan Haes Alhelou, 2021. "Enhancement of Induction Motor Dynamics Using a Novel Sensorless Predictive Control Algorithm," Energies, MDPI, vol. 14(14), pages 1-28, July.
    4. 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.
    5. Said Mahfoud & Aziz Derouich & Najib El Ouanjli & Mahmoud A. Mossa & Mahajan Sagar Bhaskar & Ngo Kim Lan & Nguyen Vu Quynh, 2022. "A New Robust Direct Torque Control Based on a Genetic Algorithm for a Doubly-Fed Induction Motor: Experimental Validation," Energies, MDPI, vol. 15(15), pages 1-26, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    IM; MCSA; BD; HT; ANN-DTC;
    All these keywords.

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