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An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

Author

Listed:
  • Luqman Maraaba

    (Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Zakariya Al-Hamouz

    (Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Mohammad Abido

    (Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor’s developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88–99%.

Suggested Citation

  • Luqman Maraaba & Zakariya Al-Hamouz & Mohammad Abido, 2018. "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors," Energies, MDPI, vol. 11(3), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:653-:d:136276
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    References listed on IDEAS

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    1. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    1. Wojciech Pietrowski & Konrad Górny, 2020. "Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding," Energies, MDPI, vol. 13(14), pages 1-19, July.
    2. Gopu Venugopal & Arun Kumar Udayakumar & Adhavan Balashanmugham & Mohamad Abou Houran & Faisal Alsaif & Rajvikram Madurai Elavarasan & Kannadasan Raju & Mohammed H. Alsharif, 2023. "Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability," Energies, MDPI, vol. 16(6), pages 1-25, March.
    3. Maciej Skowron & Marcin Wolkiewicz & Teresa Orlowska-Kowalska & Czeslaw T. Kowalski, 2019. "Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors," Energies, MDPI, vol. 12(12), pages 1-20, June.
    4. Kang Wang & Ruituo Huai & Zhihao Yu & Xiaoyang Zhang & Fengjuan Li & Luwei Zhang, 2019. "Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives," Energies, MDPI, vol. 12(3), pages 1-13, February.
    5. 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.
    6. Zorig, Assam & Hedayati Kia, Shahin & Chouder, Aissa & Rabhi, Abdelhamid, 2022. "A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 196(C), pages 273-288.
    7. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
    8. Mitja Nemec & Vanja Ambrožič & Rastko Fišer & David Nedeljković & Klemen Drobnič, 2019. "Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring," Energies, MDPI, vol. 12(5), pages 1-17, February.
    9. Mateusz Dybkowski & Szymon Antoni Bednarz, 2019. "Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives," Energies, MDPI, vol. 12(17), pages 1-21, August.
    10. Luo Wang & Yonggang Li & Junqing Li, 2018. "Diagnosis of Inter-Turn Short Circuit of Synchronous Generator Rotor Winding Based on Volterra Kernel Identification," Energies, MDPI, vol. 11(10), pages 1-15, September.
    11. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Carlos López-Torres & Antoni Garcia, 2019. "Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 12(14), pages 1-15, July.
    12. Grzegorz Tarchała & Marcin Wolkiewicz, 2019. "Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive," Energies, MDPI, vol. 12(8), pages 1-20, April.

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