access icon openaccess Permanent magnet synchronous machine stator windings fault detection by Hilbert–Huang transform

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 25/06/2018, Accepted 30/07/2018, Published 22/01/2019

The Hilbert–Huang transform (HHT) is a time-frequency signal analysis method based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault detection since it is unaffected by transient conditions which might cause false alarms. The method has been demonstrated in recent years for bearing fault detection of induction machines (IM). This study explores the possibility of applying the technique to the detection of stator short-circuit faults in permanent magnet synchronous machine (PMSM). A method based on the online statistical analysis of the instantaneous frequency calculated by the HHT is proposed and demonstrated through real-time hardware-in-the-loop simulation and experimental results.

Inspec keywords: synchronous machines; machine bearings; fault diagnosis; time-frequency analysis; Hilbert transforms; statistical analysis; stators

Other keywords: Hilbert–Huang transform; reliable fault detection; time-frequency signal analysis method; HHT; empirical mode decomposition; stator short-circuit faults; online statistical analysis; permanent magnet synchronous machine stator windings fault detection; induction machines

Subjects: Other topics in statistics; Mechanical components; Synchronous machines; Integral transforms; Signal processing and detection

References

    1. 1)
      • 6. Elbouchikhi, E., Choqueuse, V., Amirat, Y., et al: ‘An efficient Hilbert–Huang transform-based bearing faults detection in induction machines’, IEEE Trans. Energy Convers., 2017, 32, (2), pp. 401–413.
    2. 2)
      • 14. Alvarez-Gonzalez, F., Griffo, A., Sen, B., et al: ‘Real-time hardware-in-the-loop simulation of permanent-magnet synchronous motor drives under stator faults’, IEEE Trans. Ind. Electron., 2017, 64, (9), pp. 6960–6969.
    3. 3)
      • 12. Urresty, J., Riba, J., Romeral, L.: ‘Diagnosis of interturn faults in PMSMs operating under non stationary conditions by applying order tracking filtering’, IEEE Trans. Power Electron., 2013, 28, (1), pp. 507–515.
    4. 4)
      • 4. Elbouchikhi, E., Choqueuse, V., Trachi, Y., et al: ‘Induction machine bearing faults detection based on Hilbert-Huang transform’. Proc. IEEE Int. Symp. on Industrial Electronics, Buzios, Brazil, June 2015, pp. 843–848.
    5. 5)
      • 5. Osman, S., Wang, W.: ‘A morphological Hilbert–Huang transform technique for bearing fault detection’, IEEE Trans. Instrum. Meas., 2016, 65, (11), pp. 2646–2656.
    6. 6)
      • 15. Wang, B., Wang, J., Griffo, A., et al: ‘A general modelling technique for a triple redundant 3 × 3-phase PMA SynRM’, IEEE Trans. Ind. Electron., 2018, 65, (11), pp. 9068–9078.
    7. 7)
      • 8. Espinosa, A.G., Rosero, J.A., Cusido, J., et al: ‘Fault detection by means of Hilbert–Huang transform of the stator current in a PMSM with demagnetization’, IEEE Trans. Energy Convers., 2010, 25, (2), pp. 312–318.
    8. 8)
      • 9. Lu, D., Qiao, W., Gong, X., et al: ‘Current-based fault detection for wind turbine systems via Hilbert–Huang transform’. Proc. IEEE Power & Energy Society General Meeting, Vancouver, Canada, July 2013.
    9. 9)
      • 13. Sen, B., Wang, J., Lazari, P.: ‘A high fidelity, computationally efficient transient model of interior permanent magnet machine with stator turn fault’, IEEE Trans. Ind. Electron., 2016, 63, (2), pp. 773–783.
    10. 10)
      • 10. Soualhi, A., Medjaher, K., Zerhouni, N.: ‘Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression’, IEEE Trans. Instrum. Meas., 2015, 64, (1), pp. 52–62.
    11. 11)
      • 3. Yuping, Z.: ‘Hilbert–Huang transform and marginal spectrum for detection of bearing localized defects’. Proc. 2006 6th World Congress on Intelligent Control and Automation (WCICA 2006), Dalian, China, 2006, pp. 5457–5461.
    12. 12)
      • 1. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Proc. Royal Soc. A: Math. Phys. Eng. Sci., 1998, 454, (1971), pp. 903–995.
    13. 13)
      • 11. Wang, C., Liu, X., Chen, Z.: ‘Incipient stator insulation fault detection of permanent magnet synchronous wind generators based on Hilbert–Huang transformation’, IEEE Trans. Magn., 2014, 50, (11).
    14. 14)
      • 2. Antonino-Daviu, J., Riera-Guasp, M., Pineda-Sanchez, M., et al: ‘A critical comparison between DWT and Hilbert–Huang-based methods for the diagnosis of rotor bar failures in induction machines’, IEEE Trans. Ind. Appl., 2009, 45, (5), pp. 1794–1803.
    15. 15)
      • 16. Wang, B., Wang, J., Griffo, A., et al: ‘Stator turn fault detection by 2nd harmonic in instantaneous power for a triple redundant fault-tolerant PM drive’, IEEE Trans. Ind. Electron., 2018, 65, (9), pp. 7279–7289.
    16. 16)
      • 7. Rosero, J., Romeral, L., Ortega, J.A., et al: ‘Demagnetization fault detection by means of Hilbert Huang transform of the stator current decomposition in PMSM’. Proc. ISIE, Alcala de Henares, Spain, 2008, pp. 172–177.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8173
Loading

Related content

content/journals/10.1049/joe.2018.8173
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading