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


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.


Fault diagnosis;Acoustic sensors;Large-scaled power machines;ICA;Neural networks


  1. 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
  2. P. Comon, "Independent component analysis - a new concept?," Signal Processing, vol. 36, pp. 287-314, 1994
  3. T. M. Cover and J. A. Thomas, Elements of information theory. Wiley, 2006
  4. S. S. Wilks, Mathematical statistics, New York, Wiley, 1962
  5. 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
  6. V. Vapnik, The nature of statistical learning theory, Springer-Verlag, 2000
  7. 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
  8. C. L. Nikias and A. P. Petropuou, Higher-order spectra analysis, Prentice-Hall, 1993
  9. 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
  10. R. Isermann, "Process fault detection based on modelling and estimation methods - A survey," Automatica, vol. 20, no. 4, pp. 387-404, 1984
  11. 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
  12. C. W. Helstrom, Statistical theory of signal detection, Pergamon Press, 1968
  13. 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