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Diagnosis of bearing defects in induction motors using discrete wavelet transform

Author

Listed:
  • N. Bessous

    (Mohamed Khider University)

  • S. E. Zouzou

    (Mohamed Khider University)

  • W. Bentrah

    (Mohamed Khider University)

  • S. Sbaa

    (Mohamed Khider University)

  • M. Sahraoui

    (Mohamed Khider University)

Abstract

The analysis of motor current signature analysis was used many years ago, but the fast Fourier transform (FFT) technique has some disadvantages under some conditions when the speed and the load torque are not constants. The FFT has problems due to a non-stationary signal if we must report accurately the frequency characteristics of the defects. Discrete wavelets transform (DWT) treats the non-stationary stator current signal, which becomes complex when it has noises. In this paper, a technique of de-noising signals is presented by the stator current based on a series of decomposition which are compared with respect to each other. We studied a normal bearings and bearings with outer and inner faults. The choice of the decomposition order was for: Daubechies, Symlets and Meyer. The limit point of determination of the levels number is presented. In addition, we look for informations about the basic defect signal on the energy stored in each level of decomposition. DWT has the ability to allow simultaneous time–frequency analysis, so it is an appropriate tool for studying transient phenomena and non-stationary signals.

Suggested Citation

  • N. Bessous & S. E. Zouzou & W. Bentrah & S. Sbaa & M. Sahraoui, 2018. "Diagnosis of bearing defects in induction motors using discrete wavelet transform," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(2), pages 335-343, April.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:2:d:10.1007_s13198-016-0459-6
    DOI: 10.1007/s13198-016-0459-6
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    Citations

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    Cited by:

    1. 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).
    2. Muhammad Amir Khan & Bilal Asad & Karolina Kudelina & Toomas Vaimann & Ants Kallaste, 2022. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art," Energies, MDPI, vol. 16(1), pages 1-54, December.
    3. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
    4. Kuo Chi & Jianshe Kang & Fei Zhao & Long Liu, 2019. "An adaptive underdamped stochastic resonance based on NN and CS for bearing fault diagnosis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 437-452, June.

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