The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Acoustic Sensors based Fault Diagnosis Algorithm for Large-scaled Power Machines using Neural Independent Component Analysis

신경회로망 독립성분해석을 이용한 음향센서 기반 대전력기기의 고장진단 알고리즘

  • Published : 2008.05.01
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Abstract

We present a novel fault diagnosis methodology using acoustic sensor systems and neural independent component analysis for large-scaled power machines. Acoustic sensors are carried out to measure sounds generated from power machines whose signal is used to determine whether fault is occurred or not. Acoustic measurements are independently mixed and deteriorated from original source signals. We propose a demixing algorithm against such mixed signals by means of independent component analysis which is achieved based on information theory and higher-order statistics to derive learning mechanism.

Keywords

References

  1. R. Salat and S. Osowski, "Accurate fault location in the power transmission line using support vector machine approach," IEEE Trans. on Power Systems, vol. 19, no. 2, pp. 979-986, 2004 https://doi.org/10.1109/TPWRS.2004.825883
  2. V. Vapnik, The nature of statistical learning theory, Springer-Verlag, 2000
  3. G. J. Atkinson, B. C. Mecrow, A. G. Jack, D. J. Atkinson, P. Sangha, and M. Benarous, "The analysis of losses in high-power fault-tolerant machines for aerospace applications," IEEE Trans. on Industry Applications, vol. 42, no. 5, pp. 1162-1170, 2006 https://doi.org/10.1109/TIA.2006.880869
  4. Y. Murphey, M. Masrur, Z. Chen, and B. Zhang, "Model-based fault diagnosis in electric drives using machine learning," IEEE/ASME Trans. on Mechatronics, vol. 11, no. 3, pp. 290-303, 2006 https://doi.org/10.1109/TMECH.2006.875568
  5. E. Mohamed, A. Abdelaziz, and A. Mostafa, "A neural network-based scheme for fault diagnosis of power transformers," Electric Power Systems Research, vol. 75, no. 1, pp. 29-39, 2005 https://doi.org/10.1016/j.epsr.2004.11.013
  6. W. Roux, G. Ronald, and T. Habetler, "Detecting rotor faults in low power permanent magnet synchronous machines," IEEE Trans. on Power Electronics, vol. 22, no. 1, pp. 322-328, 2007 https://doi.org/10.1109/TPEL.2006.886620
  7. R. Isermann, "Process fault detection based on modelling and estimation methods - A survey," Automatica, vol. 20, no. 4, pp. 387-404, 1984 https://doi.org/10.1016/0005-1098(84)90098-0
  8. X. Tu, L.-A. Dessaint, M. El Kahel, and A. Barry, "A new model of synchronous machine internal faults based on winding distribution," IEEE Trans. on Industrial Electronics, vol. 53, no. 6, pp.1818-1827, 2006 https://doi.org/10.1109/TIE.2006.885125
  9. P. Comon, "Independent component analysis - a new concept?," Signal Processing, vol. 36, pp. 287-314, 1994 https://doi.org/10.1016/0165-1684(94)90029-9
  10. T. M. Cover and J. A. Thomas, Elements of information theory. Wiley, 2006
  11. S. S. Wilks, Mathematical statistics, New York, Wiley, 1962
  12. C. L. Nikias and A. P. Petropuou, Higher-order spectra analysis, Prentice-Hall, 1993
  13. C. W. Helstrom, Statistical theory of signal detection, Pergamon Press, 1968