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Temporal envelope detection by the square root of the three-phase currents for IM rotor fault diagnosis

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Abstract

This paper deals with a reliable and effective method for induction machine rotor fault diagnosis, using three-phase stator currents. Through a theoretical demonstration based on the magnetic field approach, it has been shown that broken rotor bars produce amplitude modulation in the stator current which can be extracted with very low complexity by calculating the root of the squared three-phase stator currents. The theoretical background of the proposed method is presented then experimentally confirmed by using three currents measured from a test bench that contains three motors, one healthy and two other with broken bars. Each motor is subjected to two load conditions (low and high). The obtained results show the robustness and effectiveness of the proposed technique, even at low-load conditions, comparing to the classical MCSA method.

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References

  1. Sharma A, Mathew L, Chatterji S (12–13 Aug 2017) Analysis of broken rotor bar fault diagnosis for induction motor. In: Proceedings of the international conference on innovations in control, communication and information system (ICICCI), Greater Noida

  2. Thorsen OV, Dalva M (1995) A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminal, and oil refineries. IEEE Trans Ind Appl 31(5):1186–1196

    Article  Google Scholar 

  3. Laala W, Guedidi S, Zouzou S (2011) Novel approach for diagnosis and detection of broken bar in induction motor at low slip using fuzzy logic. IEEE Trans Ind Electron 2011:511–516

    Google Scholar 

  4. Trachi Y, El Bouchikhi E, Choqueuse V, Benbouzid M (2016) Induction machines fault detection based on subspace spectral estimation. IEEE Trans Ind Electron 63(9):5641–5651

    Article  Google Scholar 

  5. Benbouzid M, Kliman G (2003) What stator current processing based technique to use for induction motor rotor faults diagnosis. IEEE Trans Energy Convers 18(2):238–244

    Article  Google Scholar 

  6. Rahman M, Uddin N (2015) Online unbalanced rotor fault detection of an IM drive based on both time and frequency domain analyses. In: International computing advance conference

  7. Hamdani S, Touhami O, Ibtiouen R, Fadel M (2011) Neural network technique for induction motor rotor faults classification—dynamic eccentricity and broken bar faults. In: Proceedings of the IEEE 8th international symposium on diagnostics for electrical machines, power electronics and drives, 5–8 Sept, Bologna

  8. Khelfi H, Hamdani S (2018) Stator current demodulation using square roots current stator for inverter-fed induction motor at low load conditions. In: International conference on communications and electrical engineering (ICCEE), Dec 16–17th

  9. Sharma A, Chatterji S, Mthew L (2017) A Novel Park’s vector approach for investigation of incipient stator fault using MCSA in three-phase induction motors. İn: Proceedings of IEEE international conference on innovation in control, communication and information system (ICICCI), Greater Noida, 12–13 Aug

  10. Bouslimani S, Drid S, Chrifi-Alaoui L, Bussy P, Hamzaoui M (2016) Inter-turn faults detection using park vector strategy. In: Proceedings of the IEEE international conference of automatic, control and computer engineering (STA), Sousse, 19–21 Dec

  11. Asad B, Vaimann T, Belahcen A, Kallaste A (2018) Broken rotor bar fault diagnostic of inverter fed induction motor using FFT, Hilbert and Park’s vector approach. In: Proceedings of the IEEE international conference on electrical machines (ICEM), Alexandroupoli, 3–6 Sept

  12. Diallo D, Benbouzid MEH, Hamad D, Pierre X (2005) Fault detection and diagnosis in an induction machine drive: a pattern recognition approach based on concordia stator mean current vector. IEEE Trans Energy Convers 20(3):512–519

    Article  Google Scholar 

  13. Onel IY, Benbouzid MEH (2008) Induction motor bearing failure detection and diagnosis: park and concordia transform approaches comparative study. IEEE/ASME Trans Mechatron 13(2):257–262

    Article  Google Scholar 

  14. Arkan M, Perovic DK, Unsworth P (2001) Online stator fault diagnosis in induction motors. IEE Proc Electric Power Appl 148(6):537–547

    Article  Google Scholar 

  15. Tallam RM, Habetler TG, Harley RG (2002) Stator winding turn-fault detection for closed-loop induction motor drives. In: IEEE industry applications society annual meeting, pp 1553–1557

  16. Yaghobi H (2017) Stator turn-to-turn fault detection of induction motor by non-invasive method using generalized regression neural network. Iran J Electr Electron Eng 13(1):77–88

    Google Scholar 

  17. Choqueuse V, Benbouzid MEH, Amirat Y, Turri S (2012) Diagnosis of three-phase electrical machines using multidimensional demodulation techniques. IEEE Trans Ind Electron 59(4):2014–2023

    Article  Google Scholar 

  18. El Bouchikhi EH, Choqueuse V, Benbouzid MEH, Antonino-Daviu JA (2014) Stator current demodulation for induction machine rotor faults diagnosis. In: Proceedings of the international conference on green energy, Sfax, pp 176–181, Mar 2014

  19. Bouchikhi EHE, Choqueuse V, Benbouzid MEH (2015) Condition monitoring of induction motors based on stator currents demodulation. Int Rev Electr Eng 10(06):704–715

    Google Scholar 

  20. Khelfi H, Hamdani S, Nacereddine K, Chibani Y (2018) Stator current demodulation using Hilbert transform for inverter-fed induction motor at low load conditions. In: Proceedings of the international conference on electrical sciences and technologies in Maghreb (CISTEM), 28–31 Oct, Algiers

  21. Zhipeng F, Xiaowang C, Ming J (2019) Induction motor stator current AM–FM model and demodulation analysis for planetary gearbox fault diagnosis. IEEE Trans Ind Inform 15(4):2386–2394

    Article  Google Scholar 

  22. Jaksch I, Fuchs P (2007) Rotor cage faults detection in induction motors by motor current demodulation analysis. In: Proceedings of the IEEE international symposium on diagnostics for electric machines, power electronics and drives, 6–8 Sept, Cracow

  23. Garcia-Calva TA, Morinigo-Sotelo D, Garcia-Perez A, Camarena-Martinez D, Romero-Troncoso RJ (2019) Demodulation technique for broken rotor bar detection in inverter-fed induction motor under non-stationary conditions. IEEE Trans Energy Convers 34(3):1496–1503

    Article  Google Scholar 

  24. Singh G, Naikan VNA (2018) Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis. Mech Syst Signal Process 110:333–348

    Article  Google Scholar 

  25. Pires VF, Kadivonga M, Martins JF, Pires AJ (2013) Motor square current signature analysis for induction motor rotor diagnosis. Measurement 46(2):942–948

    Article  Google Scholar 

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Correspondence to Hamid Khelfi.

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Appendix

Appendix

See Table 4.

Table 4 Parameters of the experimental motors

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Khelfi, H., Hamdani, S. Temporal envelope detection by the square root of the three-phase currents for IM rotor fault diagnosis. Electr Eng 102, 1901–1911 (2020). https://doi.org/10.1007/s00202-020-01000-y

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