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Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

Author

Listed:
  • Jordi Burriel-Valencia

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Ruben Puche-Panadero

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Javier Martinez-Roman

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Angel Sapena-Baño

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Martin Riera-Guasp

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Manuel Pineda-Sánchez

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

Abstract

Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it—short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

Suggested Citation

  • Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3361-:d:262771
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    References listed on IDEAS

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    1. 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.
    2. Zappalá, D. & Sarma, N. & Djurović, S. & Crabtree, C.J. & Mohammad, A. & Tavner, P.J., 2019. "Electrical & mechanical diagnostic indicators of wind turbine induction generator rotor faults," Renewable Energy, Elsevier, vol. 131(C), pages 14-24.
    3. Hong-Chan Chang & Yu-Ming Jheng & Cheng-Chien Kuo & Yu-Min Hsueh, 2019. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach," Energies, MDPI, vol. 12(8), pages 1-12, April.
    4. Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    5. 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.
    6. 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.
    7. Shrinathan Esakimuthu Pandarakone & Yukio Mizuno & Hisahide Nakamura, 2019. "A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors," Energies, MDPI, vol. 12(11), pages 1-14, June.
    8. Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    9. Baoshan Huang & Guojin Feng & Xiaoli Tang & James Xi Gu & Guanghua Xu & Robert Cattley & Fengshou Gu & Andrew D. Ball, 2019. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems," Energies, MDPI, vol. 12(8), pages 1-23, April.
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