access icon openaccess Detection of mechanical resonance frequencies for interior permanent magnet synchronous motor servo drives based on wavelet multiresolution filter

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 25/09/2019, Accepted 09/06/2020, Revised 18/05/2020, Published 11/06/2020

To realise the resonant characteristics of motor drive system, a wavelet multiresolution filter (WMF)-based scheme is proposed in this study to perform the detection of the mechanical resonant frequencies for interior permanent magnet synchronous motor servo drives. In a conventional motor drive system, the compliance in the coupling between motor and load may cause resonance. Moreover, the parasitic torque ripples resulting from the harmonics of a pulse-width modulation inverter may excite mechanical vibrations. In the proposed scheme, the localisation analysis of signal can be completed with the WMF at any time and frequency domains for the extraction of resonant frequencies. Then, a band-pass filter is applied to perform the frequency sweeping for the detection of resonant frequencies. In the final stage, the signal smoothing is implemented with the calculation of absolute and average values. To verify the effectiveness of the proposed resonant-frequency detection scheme using the WMF, some experimental results at different rotor speed and load conditions are provided. In addition, a two-channel dynamic signal analyser is also adopted in the experimentation for the comparison of resonant-frequency detection.

Inspec keywords: synchronous motors; vibrations; rotors; permanent magnet motors; servomotors; motor drives; torque; wavelet transforms; band-pass filters; PWM invertors; machine control

Other keywords: mechanical resonant frequencies; resonant-frequency detection scheme; interior permanent magnet synchronous motor servo drives; mechanical resonance frequencies; wavelet multiresolution filter-based scheme; conventional motor drive system; WMF; resonant characteristics

Subjects: Control of electric power systems; Synchronous machines; Drives

References

    1. 1)
      • 10. Burrus, C.S., Gopinath, R.A., Guo, H.: ‘Introduction to wavelets and wavelet transforms’ (Prentice-Hall, NJ, 1998).
    2. 2)
      • 16. Lin, F.J., Liu, Y.T., Yu, W.A.: ‘Power perturbation based MTPA with an online tuning speed controller for an IPMSM drive system’, IEEE Trans. Ind. Electron., 2018, 65, (5), pp. 3677–3687.
    3. 3)
      • 8. Venkatesan, C., Karthigaikumar, P., Paul, A., et al: ‘ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications’, IEEE Access, 2018, 6, pp. 9767–9773.
    4. 4)
      • 4. Hamadache, M., Lee, D.: ‘Rotor speed-based bearing fault diagnosis (RSB-BFD) under variable speed and constant load’, IEEE Trans. Ind. Electron., 2015, 62, (10), pp. 6486–6495.
    5. 5)
      • 12. Macías, J.A.R., Expósito, A.G.: ‘Efficient computation of the running discrete Haar transform’, IEEE Trans. Power Deliv., 2006, 21, (1), pp. 504–505.
    6. 6)
      • 7. Zhao, Z., Liu, C., Li, Y., et al: ‘Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks’, IEEE Access, 2019, 7, pp. 34060–34067.
    7. 7)
      • 9. Sangeetha, P., Hemamalini, S.: ‘Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor’, IET Signal Process., 2017, 11, (5), pp. 604–612.
    8. 8)
      • 13. Hong, Y.Y., Wang, C.W.: ‘Switching detection/classification using discrete wavelet transform and self-organizing mapping network’, IEEE Trans. Power Deliv., 2005, 20, (2), pp. 1662–1668.
    9. 9)
      • 5. Chen, C.I., Chen, Y.C.: ‘Intelligent identification of voltage variation events based on IEEE Std. 1159-2009 for SCADA of distributed energy system’, IEEE Trans. Ind. Electron., 2015, 62, (4), pp. 2604–2611.
    10. 10)
      • 14. Agaskar, A., Lu, Y.M.: ‘A spectral graph uncertainty principle’, IEEE Trans. Inf. Theory, 2013, 59, (7), pp. 4338–4356.
    11. 11)
      • 3. Yang, S.M., Wang, S.C.: ‘The detection of resonance frequency in motion control systems’, IEEE Trans. Ind. Appl., 2014, 50, (5), pp. 3423–3427.
    12. 12)
      • 2. Devaney, M.J., Eren, L.: ‘Detecting motor bearing faults’, IEEE Instrum. Meas. Mag.’, 2004, 7, (4), pp. 30–50.
    13. 13)
      • 6. Chen, C.I.: ‘Virtual multifunction power quality analyzer based on adaptive linear neural network’, IEEE Trans. Ind. Electron., 2012, 59, (8), pp. 3321–3329.
    14. 14)
      • 15. Jam, M.M., Sadjedi, H.: ‘Design and evaluation of optimal orthogonal wavelet with the least length of wavelet filters using spectral matching’, IEEE Access, 2018, 6, pp. 57414–57424.
    15. 15)
      • 1. Houari, A., Bouabdallah, A., Djerioui, A., et al: ‘An effective compensation technique for speed smoothness at low-speed operation of PMSM drives’, IEEE Trans. Ind. Appl., 2018, 54, (1), pp. 647–655.
    16. 16)
      • 11. Vetterli, M., Kovacevic, J.: ‘Wavelets and subband coding’ (Prentice-Hall, NJ, 1995).
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.1114
Loading

Related content

content/journals/10.1049/joe.2019.1114
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading