Investigation of The Effects of Eccentricity on Induction Motor via Multi-Resolution Wavelet Analysis

Yıl 2018, , 187 - 197, 03.08.2018

Abdullah Polat Abdurrahman Yılmaz Lale T. Ergene

https://doi.org/10.5152/iujeee.2018.1821

Öz

DOI: 10.5152/iujeee.2018.1821

 In
this paper, a 2.2 kW squirrel cage induction motor having different degrees of
static eccentricity is analyzed for fault detection using multi-resolution
wavelet analysis (MRWA). The reference squirrel cage induction motor was
simulated by using finite element method (FEM) and verified with the test
results for the reference. Multi-resolution wavelet analysis was applied to
decompose the high frequency components of flux density and current of the
induction motor with different levels of eccenctricity. The effects of these
components on the torque and vibration characteristics were also examined. The
results show that MRWA could be a good alternative to fast fourier transform
(FFT) in detecting air gap eccentricity in motors.

Anahtar Kelimeler

Induction motor, eccentricity, finite element method, multi-resolution wavelet analysis, diagnosis

Investigation of The Effects of Eccentricity on Induction Motor via Multi-Resolution Wavelet Analysis

Öz

DOI: 10.5152/iujeee.2018.1821

 In this paper, a 2.2 kW squirrel cage induction motor having different degrees of static eccentricity is analyzed for fault detection using multi-resolution wavelet analysis (MRWA). The reference squirrel cage induction motor was simulated by using finite element method (FEM) and verified with the test results for the reference. Multi-resolution wavelet analysis was applied to decompose the high frequency components of flux density and current of the induction motor with different levels of eccenctricity. The effects of these components on the torque and vibration characteristics were also examined. The results show that MRWA could be a good alternative to fast fourier transform (FFT) in detecting air gap eccentricity in motors.

Anahtar Kelimeler

Induction motor, eccentricity, finite element method, multi-resolution wavelet analysis, diagnosis

Kaynakça

• 1. G.M. Joksimovic, M.Duvoric, J. Penman, N. Arthur, “Dynamic simulation of dynamic eccentricity in induction machines - Winding Function Approach”, IEEE Transaction on. 143- 148.
• 2. S. Nandi, R. M. Bharadwaj, H. A. Toliyat, “Mixed Eccentricity in Three Phase İnduction Machines: Analysis, Simulation and Experiments”, Industry Applications Conference, 2002, pp. 1525-1532.
• 3. A. Polat, Y.D. Ertuğrul, L.T. Ergene, ‘’Static, Dynamic and Mixed Eccentricity of Induction Motor’’, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2015, pp. 284-288.
• 4. A. J. Ellison, S. J. Yang, ‘’Effects of rotor eccentricity on acoustic noise from induction machines’’, Proc. IEE, vol. 118, no. 1, 1971, pp. 174-184.
• 5. S. P. Verma and R. Natarajan, ‘’Effects of eccentricity in induction motors’’, Proceedings of International Conference on Electrical Machines, vol. 3, 1982, pp. 930-933.
• 6. A. C. Smith, D. G. Dorrell, ‘’Calculation and measurement of unbalanced magnetic pull in cage induction motors with eccentric rotors. Part 1: Analytical model’’, IEE Proc. Electr.Power Appl, vol. 143, no. 3, 1996, pp. 193-201.
• 7. J. R. Cameron, W. T. Thomson, A. B. Dow, ‘’Vibration and current monitoring for detecting airgap eccentricity in large induction motors’’, IEE Proceedings B Electric Power Applications, vol. 133, no.3, 1986, pp. 155-163.
• 8. S. Salon, K. Sivasubramaniam, L.T. Ergene, ‘’The Effect of Asymmetry on Torque in Permanent Magnet Motors’’, The Proceedings of IEEE International Electric Machines and Drives Conference, 2001, pp. 208-217.
• 9. A. Polat, Y.D. Ertuğrul, L.T. Ergene, ‘’Analysis of Static Eccentricity in Squirrel Cage Induction Motors’’, The Proceedings of IEEE International Conference on Power Engineering Energy and Electrical Drives, 2013, pp. 1504-1508.
• 11. S. Nandi, S. Ahmed, H.A. Toliyat, ‘’Detection of Rotor Slot and Other Eccentricity Related Harmonics in a Three Phase Induction Motor with Different Rotor Cages, IEEE Transactions on Energy Conversion, vol. 16, no. 3, 2001, pp. 253-260.
• 12. P. Frauman, A. Burakov, A. Arkkio, ‘’Effects of the Slot Harmonics on the Unbalanced Magnetic Pull in an Induction Motor With an Eccentric Rotor, IEEE Transactions on Magnetics, vol. 43, No. 8, 2007, pp. 3441-3444.
• 13. Z. Chen, J. Meng, B. Liang, D. Guo, “Fault Diagnosis of AC Squirrel-cage Asynchronous Motors based on Wavelet Packet-Neural Network”, EUROCON, 2007.
• 14. H. Bae, Y.T. Kim, S.H. Lee, S. Kim, ‘’Fault Diagnostic of İnduction Motors for Equipment Reliability and Health Maintenance Based upon Fourier and Wavelet Analysis’’, Artifical Life and Robotics, vol.9, no. 3, 2005, pp.112-116.
• 15. A. Bouzida, O. Touhami, R. Ibtiouen, A. Belouchrani, M. Fadel, A. Rezzoug, ‘’ Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform’’, IEEE Transactions on Industrial Electronics, vol. 58, no. 9, 2011, pp. 4385-4395.
• 16. S.G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, IEEE, 1989.
• 17. I.P. Georgokopuolos, E.D. Mitronikas, A.N. Safacas, I.P. Tsoumas, “Detection of Eccentricity in Inverter-Fed Induction Machines using Wavelet Analysis of the Stator Current”, IEEE Power Electronics Specialists Conference, 2008.
• 18. J. Seshadrinath, B. Singh, B.K. Panigrahi, ‘’Single-Turn Fault Detection in Induction Machine Using Complex-Wavelet-Based Method’’, IEEE Transactions on Industry Applications, vol. 48, no. 6, 2012, pp. 1846-1854.
• 19. G.A. Jimenez, A.O. Munoz, M.A. Duarte-Mermoud ‘’Fault detection in induction motors using hilbert and wavelet transforms’’, Electrical Engineering, vol.89, no. 3, 2007,pp. 205-220.
• 20. J.P. Llinares, J.A. Daviu, J.R. Folch, D.M. Sotelo, O.D. Perez, ‘’Eccentricity Diagnosis in Inverter - Fed Induction Motors via the Analytic Wavelet Transform of Transient Currents’’, IEEE International Conference on Electrical Machines, 2010.
• 21. E. Ayaz, A. Öztürk, S. Şeker, ‘’Continuous Wavelet Transform for Bearing Damage Detection in Electric Motors, MELECON- IEEE Mediterranean Electrotechnical Conference, 2006, pp. 1130-1133.
• 22. D. Bayram, S. Şeker, ‘’Wavelet Based Neuro-Detector for Low Frequencies of Vibration Signals in Electric Motors”, Applied Soft Computing, vol.13, no. 5, 2013, pp. 2683-2691.
• 23. J. Rosero, J.L. Romeral, J. Cusida, J. A. Ortega, A. Garcia, “Fault Detection of Eccentricity and Bearing Damage in a PMSM by means of Wavelet Transforms Decomposition of the Stator Current”, IEEE Applied Power Electronics Conference and Exposition, 2008, pp. 1048-2334.
• 24. N. Bianchi, ‘’Electrical Machine Analysis Using Finite Elements’’, CRC Press, Taylor & Francis Group, 2005.
• 25. C. Yılmaz, M. İşcan, A. Yılmaz, ‘’A Fully Automatic Novel Method to Determine QT Interval Based on Continuous Wavelet Transform’’, IU - JEEE, vol. 17, no. 1, 2017, pp. 3093-3099.