INTELLIGENT METHOD OF ELECTRIC DRIVE DIAGNOSTIC WITH DUE ACCOUNT FOR ITS OPERATION MODE

  • Alexey Grigoryevich Bulgakov Southwest State University, Kursk, Russia
  • Tatyana Nikolaevna Kruglova South-Russian State Polytechnic University, Novocherkassk, Rusia

Abstract


In this article is proposed an intelligent method for diagnosing a technical condition, which makes it possible to distinguish a true malfunction of object from changing the parameters of its operating mode. As a result of numerous experiments has been revealed the dependence of measurement of wavelet transformation coefficients on the characteristic scales of a serviceable and faulty engine under different loading regimes. On the basis of the received information has been developed a neural classification network which makes it possible to reveal the current state of the object. Further studies have shown that any parent wavelet can be used to implement the proposed method. The study of the state of the drive under various loads confirms the correctness of the theoretical calculations and the adequacy of the model.

References

T.W. Körner. Fourier Analysis. - Cambridge University Press, 1988.

Daubechies. The wavelet transform time-frequency localization and signal analysis. IEEE Trans. Inform. Theory, 1992,pp. 961–1004.

T.N. Kruglova. Intelligent Diagnosis of the Electrical Equipment Technical Condition // Procedia Engineering, 2015, Vol 129, pp. 219–224.

Peter W. Tsea, Wen-xian Yangb, H.Y. Tama. Machine fault diagnosis through an effective exact wavelet analysis // Journal of Sound and Vibration 277, 2004, pp. 1005–1024.

B. Liu, S.F. Ling.On the selection of informative wavelets for machinery diagnosis // Mechanical Systems and Signal Processing 13 (1), 1999, pp. 145–162.

Subhasis Nandi, Hamid A. Toliyat, and Xiaodong Li.Condition Monitoring and Fault Diagnosis of Electrical Motors – A Review, IEEE Conference Transactions on energy conversion, Vancouver, Canada // Journals & Magazines Vol. 20 (4), December 2005, pp. 719-729.

Yoon-Seok Lee, Kyung-Tae Kim, Jin Hur.Finite-Element Analysis of the Demagnetization of IPM-Type BLDC Motor With Stator Turn Fault // IEEE transactions on magnetics, vol. 50 (2), pp 7022004- 7022004.

M. Shakouhi, M. Mohamadian, E. Afjei Fault. Tolerant Control of Brushless DC Motors Under Static Rotor Eccentricity // IEEE transactions on industrial electronics, vol. 62 (3), 2015 pp. 1400-1409.

Z.K. Penga, Peter W. Tsea, F.L. Chub A comparison study of improved Hilbert–Huang trans-form and wavelet transform: Application to fault diagnosis for rolling bearing // Mechanical Sys-tems and Signal Processing 19, 2005, pp. 974–988.

M.A. Awadallah and M.M. Morcos Diagnosis of Stator Short Circuits in Brushless DC Motors by Monitoring Phase Voltages // IEEE Transactions on Energy Conversion, 2005, vol. 20 (1), pp. 2460–247.

T.J. Andersen and B.M. Wilamowski. A modified regression algorithm for fast one layer neural network training // World Congress of Neural Networks, vol. 1, pp. 687–690, Washington, DC, July 17–21, 1995.

Published
2017/12/15
Section
Original Scientific Paper