DOI QR코드

DOI QR Code

Improved Mechanical Fault Identification of an Induction Motor Using Teager-Kaiser Energy Operator

  • Agrawal, Sudhir (Madan Mohan Malaviya University of Technology) ;
  • Giri, V.K. (Madan Mohan Malaviya University of Technology)
  • Received : 2017.02.15
  • Accepted : 2017.06.04
  • Published : 2017.09.01

Abstract

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

Keywords

Bearing Damage Index (BDI);Kurtosis;Power Spectral Density(PSD);Skewness;Teager-Kaiser Energy Operator (TKEO)

Acknowledgement

Supported by : M.M.M. University of Technology

References

  1. A. K. S. Jardine, D. Lin, and D. Banjevic, "A Review on Mchinery Diagnostics and Prognostics Implementing Condition-Based Maintenance," Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483-1510, Oct. 2006. https://doi.org/10.1016/j.ymssp.2005.09.012
  2. S. A. Mortazavizadeh and S. M. G. Mousavi, "A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines," Phys. Sci. Int. J., vol. 4, no. 3, pp. 310-338, 2014. https://doi.org/10.9734/PSIJ/2014/4837
  3. G. K. Singh, A. Saleh, and A. Kazzaz, "Induction Machine Drive Condition Monitoring and Diagnostic Research-A Survey," Electr. Power Syst. Res., vol. 64, no. 2, pp. 145-158, 2003. https://doi.org/10.1016/S0378-7796(02)00172-4
  4. M. Tsypkin, "Induction Motor Condition Monitoring: Vibration Analysis Technique-a Practical Implementation," in 2011 IEEE International Electric Machines & Drives Conference, 2011, pp. 406-411.
  5. D.-H. Hwang, Y.-W. Youn, J.-H. Sun, K.-H. Choi, J.-H. Lee, and Y.-H. Kim, "Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals," J. Electr. Eng. Technol., vol. 10, no. 4, pp. 1558-1565, 2015. https://doi.org/10.5370/JEET.2015.10.4.1558
  6. T. W. Rauber, F. de Assis Boldt, and F. M. Varejao, "Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis," IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 637-646, Jan. 2015. https://doi.org/10.1109/TIE.2014.2327589
  7. V. K. Rai and A. R. Mohanty, "Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform," Mech. Syst. Signal Process., vol. 21, no. 6, pp. 2607-2615, Aug. 2007. https://doi.org/10.1016/j.ymssp.2006.12.004
  8. G. A. Jimenez, A. O. Munoz, and M. A. Duarte-Mermoud, "Fault detection in induction motors using Hilbert and Wavelet transforms," Electr. Eng., vol. 89, no. 3, pp. 205-220, Feb. 2006.
  9. Q. He, "Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis," Mech. Syst. Signal Process., vol. 35, no. 1-2, pp. 200-218, Feb. 2013. https://doi.org/10.1016/j.ymssp.2012.08.018
  10. A. Mohammadi and M. S. Safizadeh, "Bearing Multiple Defects Detection Based on Envelope Detector Time Constant," J. Tribol., vol. 135, no. 1, p. 011102, Dec. 2012. https://doi.org/10.1115/1.4007806
  11. Case Western Reserve University, Bearing data center [online], Available:URL:http://www.eecs.cwru.edu/laboratory/bearing/download.htm, 2016
  12. S. Hesari and A. Hoseini, "A New Approach to Improve Induction Motor Performance in Light-Load Conditions," J. Electr. Eng. Technol., vol. 12, no. 3, pp. 1195-1202, 2017. https://doi.org/10.5370/JEET.2017.12.3.1195
  13. A. Sharma, M. Amarnath, and P. Kankar, "Feature extraction and fault severity classification in ball bearings," J. Vib. Control, no. April, pp. 1-17, Apr. 2014.
  14. U. Sengamalai and S. Chinnamuthu, "An Experimental Fault Analysis and Speed Control of an Induction Motor using Motor Solver," J. Electr. Eng. Technol., vol. 12, no. 2, pp. 761-768, 2017. https://doi.org/10.5370/JEET.2017.12.2.761
  15. R. B. W. B. W. Heng and M. J. M. J. M. Nor, "Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition," Appl. Acoust., vol. 53, no. 1-3, pp. 211-226, Jan. 1998. https://doi.org/10.1016/S0003-682X(97)00018-2
  16. D. Wang and C. Shen, "An equivalent cyclic energy indicator for bearing performance degradation assessment," J. Vib. Control, no. September, Sep. 2014.
  17. P. Henriquez Rodriguez, J. B. Alonso, M. A. Ferrer, and C. M. Travieso, "Application of the Teager-Kaiser energy operator in bearing fault diagnosis," ISA Trans., vol. 52, no. 2, pp. 278-284, 2013. https://doi.org/10.1016/j.isatra.2012.12.006
  18. M. Pineda-sanchez, S. Member, J. Perez-cruz, J. Pons-llinares, V. Climente-alarcon, and J. a Antoninodaviu, "Application of the Teager-Kaiser Energy Operator to the Fault Diagnosis of Induction Motors," IEEE Trans. Energy Convers., vol. 28, no. 4, pp. 1036-1044, 2013. https://doi.org/10.1109/TEC.2013.2279917
  19. P. Maragos, J. F. Kaiser, and T. F. Quatieri, "Energy separation in signal modulations with application to speech analysis," IEEE Trans. Signal Process., vol. 41, no. 10, pp. 3024-3051, 1993. https://doi.org/10.1109/78.277799
  20. V. T. Tran, F. AlThobiani, and A. Ball, "An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks," Expert Syst. Appl., vol. 41, no. 9, pp. 4113-4122, 2014. https://doi.org/10.1016/j.eswa.2013.12.026
  21. N. Mehala and R. Dahiya, "A comparative study of FFT, STFT and wavelet techniques for induction machine fault diagnostic analysis," CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, manmachine systems and cybernetics, 2008.
  22. K. Gaeid and H. Ping, "Fault Diagnosis of Induction Motor Using MCSA and FFT," Electrical and Electronics Engineering, vol. 1, no. 2, pp. 85-92, 2011.