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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  );
  • 초록

    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.


  • 주제어

    Induction motor .   broken rotor bars .   fault detection .   Multiple Discriminant Analysis .   Artificial Neural Networks .   Multiple signature processing.  

  • 참고문헌 (9)

    1. ?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 
    2. ?S. Lawrence Marple, 'Digital Spectral Analysis with Applications', Prentice Hall, 1987 
    3. ?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 
    4. ?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 
    5. ?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 
    6. 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 
    7. ?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 
    8. ?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 
    9. ?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 

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