Application of time-frequency distribution and neural networks for fault classification in power electronics

IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Proceedings

Volumn 2003-January, Issue , 2003, Pages 67-71

  Leonowicz, Z ^a   Lobos, T ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Fault detection; Frequency conversion; Intelligent networks; Neural networks; Noise robustness; Pattern classification; Performance analysis; Signal analysis; Signal resolution; Time frequency analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; FREQUENCY CONVERTERS; INTELLIGENT NETWORKS; NEURAL NETWORKS; OPTICAL FREQUENCY CONVERSION; PATTERN RECOGNITION; PROBABILITY DISTRIBUTIONS; SIGNAL ANALYSIS; SIGNAL DETECTION;

INDUSTRIAL FREQUENCY; NOISE ROBUSTNESS; PERFORMANCE ANALYSIS; PROBABILISTIC NEURAL NETWORKS; SIGNAL CLASSIFICATION; SIGNAL RESOLUTION; TIME FREQUENCY ANALYSIS; TIME-FREQUENCY DISTRIBUTIONS;

FAULT DETECTION;

EID: 33749684075     PISSN: 21591547     EISSN: 21591555     Source Type: Conference Proceeding    
DOI: 10.1109/CIMSA.2003.1227204     Document Type: Conference Paper

References (12)

1. R. Baraniuk and D. Jones, "A signal dependent time-frequency representation: optimal kernel design", IEEE Trans. on Signal Processings, No. 41, pp. 1589-1602, 1993.
2. S. M. Kay, Modern spectral estimation: Theory and application, Englewood Cliffs: Prentice Hall 1988.
3. Z. Leonowicz and T. Łobos, "Analysis of Three-Phase Signals Using Wigner Spectrum", XVI IMEKO World Congress, Vienna, Vol. VI, pp. 215-220, 2000.
4. T. Łobos, Z. Leonowicz and J. Rezmer, "Harmonics and Interharmonics Estimation Using Advanced Signal Processing Methods", 9th IEEE Int. Conf. on Harmonics and Quality of Power, Orlando (USA), Vol. 1, pp. 335-340.,2000.
5. T. Łobos, D. Ruhm and Z. Wacławek, "Signal analysis in converter-fed drives using adaptive neural networks", Electr. Power Compon. Syst. Vol. 29 No 2, pp.105-116, 2001.
6. Matlab® ToolBox. Neural Networks. Radial Basis Networks, online http://www.mathworks.com.
7. W. Martin and P. Flandrin, "Wigner-Ville Spectral Analysis of Non-stationary Processes", IEEE Trans. on Acoustics, Speech and Signal Processing, vol. 33, no. 6, pp. 1461-1470, 1985.
8. S. Quian and D. Chen, Joint Time-Frequency Analysis - Methods and Applications, Prentice-Hall, Upper Saddle River, NJ, pp. 101-131,1996.
9. B. Tacer and P. Loughlin, "Non-stationary signal classification using the joint moments of time-frequency distributions", Pattern Recognition, Vol. 31, No. 11, pp. 1635-1641, 1998.
10. M. Till and S. Rudolph, "Optimised time-frequency distributions for acoustic signal classification", Proc. SPIE Aerosense Conference on Wavelet Applications VIII, Orlando, Florida, 2001.
11. P. D. Wasserman, Advanced Methods in Neural Computing, New York: Van Nostrand Reinhold, 1993.
12. L. Atlas, J. Droppo and J. McLaughlin, "Optimizing Time-Frequency Distributions for Automatic Classification", Proceedings SPIE, vol. 3162, 1997.