Sleeve Bearing Oil Whirl Fault Diagnosis In Three Phase Induction Motor using Hilbert - Huang Transform

Document Type : Original Article

Authors

Faculty of Engineering, University of Zanjan, Zanjan, Iran

Abstract

Induction motors play an indispensable role in setting up the industry’s wheel and production cycle. Therefore, much research is being done to improve their construction and production and to monitor their condition. This paper aims to study and investigate a common fault related to these motors, i.e. the oil whirl fault within their sleeve bearing, and try to provide a useful and effective solution in order to diagnose this fault. For this purpose, firstly, an induction motor that has one of its bearings with oil whirl fault, is modeled and simulated by appropriate definition of the air gap function and using the modified winding function approach. Then, the stator current is obtained under both the healthy and faulty conditions to detect an effective and non-invasive method for the fault diagnosis. The stator current signal is processed using a combination of the empirical mode decomposition and Hilbert transform, called Hilbert-Huang transform and a suitable index for detecting oil whirl fault is proposed. Then, the efficiency of the proposed index is evaluated by applying it to the stator current signal obtained from the practical induction motor and its efficiency is proved in practice.

Keywords

References

[1]      S. Singhal and R. Mistry, “Oil whirl rotor dynamic instability phenomenon-diagnosis and cure in large induction motor,” Proc. of IEEE IAS PCIC, pp. 1-8, Sep. 2009.
[2]      M. Ojaghi and N. Yazdandoost, “Oil-whirl fault modeling, simulation, and detection in sleeve bearings of squirrel cage induction motors” IEEE Transactions on Energy Conversion, vol. 30, no. 4, December 2015.
[3]       منصور اوجاقی، ناصر یزدان دوست، « تشخیص عیب گردش روغن در یاتاقان لغزشی موتورالقایی با استفاده از هارمونیک­های توان لحظه­ای»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 4، صفحات 7 تا 17، زمستان 1395.
[4]      M. Ojaghi, and N. Yazdandoost, “Winding function approach to simulate induction motors under sleeve bearing fault,” in Proc. IEEE Int. Conf. Industrial Technology, pp. 158–163, 2014.
[5]      J. Jung, Y. Park, S. B. Lee, C. Cho, K. Kim, E. Wiedenbrug and M. Teska, “Monitoring journal bearing faults making use of motor current signature analysis for induction motors,” IEEE Industry Applications Magazine, pp. 12-21, Jul./Aug. 2017.
[6]      S. Osman and W. Wang, “A morphological Hilbert-Huang transform technique for bearing fault detection,” IEEE Trans. Instrum. Meas, vol. 65, no. 11, pp. 2646-2656, Nov. 2016.
[7]      E. Elbouchikhi, V. Choqueuse, Y. Amirat, M. Benbouzid and S. Turri, “An efficient Hilbert–Huang transform-based bearing faults detection in induction machines,” IEEE Trans. Energy Conversion, vol. 32, no. 2, pp. 401-413, Jun. 2017.
[8]      A. Verma1, S. Sarangi, M. Kolekar and S. Banerjee, “Oil whip detection using stator current monitoring,” IEEE Symposium on Computers & Informatics, 2012.
[9]      J. E. Berry, “Oil whirl and whip instabilities – within journal bearing,” https://www.machinerylubrication.com/Read/754/oil-whirl-whip, May 2005.
[10]      H. A. Toliyat, S. Nandi, S. Choi and H. Meshgin-Kelk, Electric Machines, Modeling, Condition Monitoring and Fault Diagnosis, CRC Press, 2013.
[11]      N.E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. Yen, C. C. Tung and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. Roy. Soc. Lond. A, vol. 454, pp. 903-995, 1998.
[12]      T.-Y. Wu, C.-C. Wang, and Y.-L. Chung, “The bearing fault diagnosis of rotating machinery by using Hilbert–Huang transform,” in Proc. Int. Conf. Electric Inform. Control Eng. (ICEICE), pp. 6238–6241, 2011.
[13]      C. Li, L. Zhan and L. Shen, “Friction signal denoising using complete ensemble EMD with adaptive noise and mutual information”, Entropy, vol. 17, pp. 5965-5979, 2015.
TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 49, Issue 4 - Serial Number 90
February 2020
Pages 1483-1494

Files

Share

How to cite

Statistics