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Ehsani-Seresht, Abbas, Bolourian, Ali, RoshanFekr, Reza. (1401). Detection of the Misalignment Fault in Non-Electric Rotating Machines Through the Current Signal of a Brushless Direct Current Motor. هنرهای کاربردی, 2(3), 11-17. doi: 10.22075/mseee.2023.28937.1121
Abbas Ehsani-Seresht; Ali Bolourian; Reza RoshanFekr. "Detection of the Misalignment Fault in Non-Electric Rotating Machines Through the Current Signal of a Brushless Direct Current Motor". هنرهای کاربردی, 2, 3, 1401, 11-17. doi: 10.22075/mseee.2023.28937.1121
Ehsani-Seresht, Abbas, Bolourian, Ali, RoshanFekr, Reza. (1401). 'Detection of the Misalignment Fault in Non-Electric Rotating Machines Through the Current Signal of a Brushless Direct Current Motor', هنرهای کاربردی, 2(3), pp. 11-17. doi: 10.22075/mseee.2023.28937.1121
Ehsani-Seresht, Abbas, Bolourian, Ali, RoshanFekr, Reza. Detection of the Misalignment Fault in Non-Electric Rotating Machines Through the Current Signal of a Brushless Direct Current Motor. هنرهای کاربردی, 1401; 2(3): 11-17. doi: 10.22075/mseee.2023.28937.1121

Detection of the Misalignment Fault in Non-Electric Rotating Machines Through the Current Signal of a Brushless Direct Current Motor

Journal of Modeling and Simulation in Electrical and Electronics Engineering
دوره 2، شماره 3 - شماره پیاپی 9، بهمن 2022، صفحه 11-17    اصل مقاله (1.51 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/mseee.2023.28937.1121
نویسندگان
Abbas Ehsani-Seresht* 1؛ Ali Bolourian1؛ Reza RoshanFekr2
1Department of Mechanical Engineering, Hakim Sabzevari University, Sabzevar, Iran
2Department of Electrical Engineering, Hakim Sabzevari University, Sabzevar, Iran
تاریخ دریافت: 18 آبان 1401،  تاریخ بازنگری: 14 بهمن 1401،  تاریخ پذیرش: 10 تیر 1402 
چکیده
One of the most common causes of vibration in rotating machines is the misalignment fault. The Motor Current Signature Analysis (MCSA) is an excellent method for the detection of the misalignment fault on those electric machines whose current signals are practically available. This paper aims to extend the application of the MCSA method to non-electric rotating systems for the detection of the misalignment fault between the driver machine and the driven machine. For this, a small brushless direct current (BLDC) motor was connected to the driver machine. Then, by using the Fast Fourier Transform and Wavelet Packet Transform the current signal of the BLDC motor was analyzed to detect the misalignment fault. In addition, a fault detection indicator was provided using the energy of the current signal. For the evaluation of the proposed method, an experimental setup was provided. The driver machine of the setup was an induction machine. So, it was possible to investigate the misalignment fault through both the BLDC motor and the induction motor. The results showed that the misalignment fault can be detected by the current signal of the BLDC motor as well as the current signal of the driver machine.
کلیدواژه‌ها
Condition monitoring؛ Fault detection؛ Fast Fourier Transform؛ Wavelet packet transform
مراجع
  1. Verma, A.K., Sarangi, S. and Kolekar, M.H., 2013. Misalignment fault detection in induction motor using rotor shaft vibration and stator current signature analysis. International Journal of Mechatronics and Manufacturing Systems, 6(5-6), pp.422-436.
  2. Bossio, J.M., Bossio, G.R. and De Angelo, C.H., 2009, November. Angular misalignment in induction motors with flexible coupling. In 2009 35th Annual Conference of IEEE Industrial Electronics(pp. 1033-1038). IEEE.
  3. Gibbons, C.B., 1976. Coupling misalignment forces. In Proceedings of the 5th Turbomachinery Symposium. Texas A&M University. Gas Turbine Laboratories.
  4. Sinha, J.K., Lees, A.W. and Friswell, M.I., 2004. Estimating unbalance and misalignment of a flexible rotating machine from a single run-down. Journal of Sound and Vibration, 272(3-5), pp.967-989.
  5. Prabhu, B.S., 1997. An experimental investigation on the misalignment effects in journal bearings. Tribology transactions, 40(2), pp.235-242.
  6. Bahaloo, H., Ebrahimi, A. and Samadi, M., 2009, January. Misalignment modeling in rotating systems. In Turbo Expo: Power for Land, Sea, and Air(Vol. 48876, pp. 973-979).
  7. Chacon, J.L.F., Andicoberry, E.A., Kappatos, V., Asfis, G., Gan, T.H. and Balachandran, W., 2014. Shaft angular misalignment detection using acoustic emission. Applied acoustics, 85, pp.12-22.
  8. Caso, E., Fernandez-del-Rincon, A., Garcia, P., Iglesias, M. and Viadero, F., 2020. Monitoring of misalignment in low speed geared shafts with acoustic emission sensors. Applied Acoustics, 159, p.107092.
  9. Chacon, J.L.F., Andicoberry, E.A., Kappatos, V., Asfis, G., Gan, T.H. and Balachandran, W., 2014. Shaft angular misalignment detection using acoustic emission. Applied acoustics, 85, pp.12-22.
  10. Khan, M.A., Shahid, M.A., Ahmed, S.A., Khan, S.Z., Khan, K.A., Ali, S.A. and Tariq, M., 2019. Gear misalignment diagnosis using statistical features of vibration and airborne sound spectrums. Measurement, 145, pp.419-435.
  11. Sarkar, S., Nandi, A., Neogy, S., Dutt, J.K. and Kundra, T.K., 2010. Finite element analysis of misaligned rotors on oil-film bearings. Sadhana, 35(1), pp.45-61.
  12. Xu, M. and Marangoni, R.D., 1994. Vibration analysis of a motor-flexible coupling-rotor system subject to misalignment and unbalance, part I: theoretical model and analysis. Journal of sound and vibration, 176(5), pp.663-679.
  13. Hili, M.A., Fakhfakh, T., Hammami, L. and Haddar, M., 2005. Shaft misalignment effect on bearings dynamical behavior. The International Journal of Advanced Manufacturing Technology, 26(5-6), pp.615-622.
  14. Verucchi, C., Bossio, J., Bossio, G. and Acosta, G., 2016. Misalignment detection in induction motors with flexible coupling by means of estimated torque analysis and MCSA. Mechanical Systems and Signal Processing, 80, pp.570-581.
  15. Chaudhury, S.B., Sengupta, M. and Mukherjee, K., 2013. Experimental study of induction motor misalignment and its online detection through data fusion. IET Electric Power Applications, 7(1), pp.58-67.
  16. Vilchis-Rodriguez, D.S., Djurović, S. and Smith, A.C., 2013. Wound rotor induction generator bearing fault modelling and detection using stator current analysis. IET Renewable Power Generation, 7(4), pp.330-340.
  17. Jung, J., Park, Y., Lee, S.B., Cho, C.H., Kim, K., Wiedenbrug, E.J. and Teska, M., 2017. Monitoring journal-bearing faults: making use of motor current signature analysis for induction motors. IEEE Industry Applications Magazine, 23(4), pp.12-21.
  18. Kar, C. and Mohanty, A.R., 2008. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform. Journal of Sound and Vibration, 311(1-2), pp.109-132.
  19. Chen, X. and Feng, Z., 2019. Time-frequency space vector modulus analysis of motor current for planetary gearbox fault diagnosis under variable speed conditions. Mechanical Systems and Signal Processing, 121, pp.636-654.
  20. Antonino-Daviu, J. and Popaleny, P., 2018, September. Detection of induction motor coupling unbalanced and misalignment via advanced transient current signature analysis. In 2018 XIII International Conference on Electrical Machines (ICEM)(pp. 2359-2364). IEEE.
  21. Randall, R.B., 2011. Vibration-based condition monitoring: industrial, aerospace and automotive applications. John Wiley & Sons.
  22. Verma, A.K., Sarangi, S. and Kolekar, M.H., 2014. Experimental investigation of misalignment effects on rotor shaft vibration and on stator current signature. Journal of Failure Analysis and Prevention, 14(2), pp.125-138.
  23. Rajagopalan, S., 2006. Detection of Rotor and Load Faults in BLDC Motors Operating Under Stationary and Non-Stationary Conditions(Doctoral dissertation, Georgia Institute of Technology).
  24. Arkan, M.Ü.S.L.Ü.M., Çaliş, H. and Tağluk, M.E., 2005. Bearing and misalignment fault detection in induction motors by using the space vector angular fluctuation signal. Electrical Engineering, 87(4), pp.197-206.
  25. Zhou, R., Bao, W., Li, N., Huang, X. and Yu, D., 2010. Mechanical equipment fault diagnosis based on redundant second-generation wavelet packet transform. Digital signal processing, 20(1), pp.276-288.
  26. Zarei, J. and Poshtan, J., 2007. Bearing fault detection using wavelet packet transform of induction motor stator current. Tribology International, 40(5), pp.763-769.
  27. Ye, Z., Wu, B. and Sadeghian, A., 2003. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. IEEE transactions on industrial electronics, 50(6), pp.1217-1228.
  28. Jiang, H., Li, C. and Li, H., 2013. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mechanical Systems and Signal Processing, 36(2), pp.225-239.
  29. Yan, R., Gao, R.X. and Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal processing, 96, pp.1-15.
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