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Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

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  • Noman Shabbir

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Lauri Kütt

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Bilal Asad

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Muhammad Jawad

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan)

  • Muhammad Naveed Iqbal

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Kamran Daniel

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
    Department of Electrical Engineering, University of Engineering & Technology, Lahore 54890, Pakistan)

Abstract

In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.

Suggested Citation

  • Noman Shabbir & Lauri Kütt & Bilal Asad & Muhammad Jawad & Muhammad Naveed Iqbal & Kamran Daniel, 2021. "Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study," Energies, MDPI, vol. 14(7), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2001-:d:530333
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    References listed on IDEAS

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    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).
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