KIEE International Transaction on Electrical Machinery and Energy Conversion Systems

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection

  • Ayhan Bulent (Dept. of Electrical and Computer Engineering North Carolina State University) ;
  • Chow Mo-Yuen (Dept. of Electrical and Computer Engineering North Carolina State University) ;
  • Song Myung-Hyun (Dept. of Electrical and Computer Engineering North Carolina State University)
  • Published : 2005.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

Keywords

References

  1. M.E.H. Benbouzid , G.B. Kliman, 'What stator current processing based technique to use for induction motor rotor faults diagnosis',' IEEE Power Engineering Review, August 2002
  2. B. Li., M.-Y. Chow, Y. Tipsuwan, lC. Hung, 'Neural-network-based motor rolling bearing fault diagnosis,' IEEE Trans. Industrial Electronics on, vol. 47, no. 5 , Oct. 2000, pp. 1060-1069 https://doi.org/10.1109/41.873214
  3. S. Altug, M.- Y. Chow, H.J. Trussell, 'Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis,' IEEE Trans. on Industrial Electronics, vol. 46, no. 6 , Dec. 1999, pp. 1069 -1079 https://doi.org/10.1109/41.807988
  4. M. Haji, H. A. Toliyat, 'Pattern recogmtion - a technique for induction machines rotor broken bar detection,' IEEE Trans. on Energy Conversion, vol. 16, no. 4, Dec. 2001, pp. 312-317 https://doi.org/10.1109/60.969469
  5. A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, G.B. Kliman, , 'Quantitative evaluation of induction motor broken bars by means of electrical signature analysis', IEEE Trans. on Industrial Applications, vol. 37, no. 5, Sep./Oct. 2001, pp. 1248-1255 https://doi.org/10.1109/28.952499
  6. G.B. Kliman et al., 'Non-invasive detection of broken rotor bars in operating induction motors', IEEE Trans. on Energy Conversion vol. EC-3, no. 4, pp. 873-879, 1988
  7. F. Filipetti et al., 'AI Techniques in induction machines diagnosis including the speed rifle effect,' lEEE- lAS Annual Meeting Conference, San Diego, pp. 655-662, Oct 6-10, 1996
  8. S. Lawrence Marple, 'Digital Spectral Analysis with Applications', Prentice Hall, 1987
  9. S. Amari et al. 'Asymptotic statistical theory of overtraining and cross validation,' IEEE Trans. on Neural Networks, vol. 8, no. 5, Sep. 1997, pp. 985-996 https://doi.org/10.1109/72.623200