A Combined fBm and PPCA Based Signal Model for On-Line Recognition of PD Signal

  • Pradeep Kumar Shetty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


The problem of on-line recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD), buried in excessive noise has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI), due to, overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, on-site, PD measurement is hardly possible in conventional frequency based DSP techniques. We provide new methods to model and recognize the PD signal, on-line. The observed noisy PD signal is modeled as linear combination of systematic and random components employing probabilistic principal component analysis (PPCA). Being a natural signal, PD exhibits long-range dependencies. Therefore, we model the random part of the signal with fractional Brownian motion (fBm) process and pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and non-parametric analysis based on smooth FIR filter is undertaken. The method proposed by the Author found to be effective in recognizing and retrieving the PD pulses, automatically, without any user interference.


Fractional Brownian Motion Partial Discharge Latent Variable Model Pulsive Interference Probabilistic Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Satish, L., Nazneen, B.: Wavelet denoising of PD signals buried in excessive noise and interference. IEEE Transaction on DEI 10(2), 354–367 (2003)Google Scholar
  2. 2.
    Tipping, M.E., Bishop, C.M.: A hierarchical latent variable model for data visualization. IEEE trans. PAMI 20(3), 25–35, 281–293 (1998)Google Scholar
  3. 3.
    Flandrin, P.: Wavelet analysis and synthesis of fractional Brownian motion. IEEE transaction on Information Theory 38(2), 910–917 (1992)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Wornell, G.: Signal Processing with Fractals: A Wavelet Based Approach, ch. 3, pp. 30–46. Prentice Hall PTR, Newjersy (1996)Google Scholar
  5. 5.
    Wornell, G.W.: A Karhunen-Loeve-like expansion for 1/f processes via wavelets. IEEE. Trans. Inform. Theory. 36, 859–861 (1990)CrossRefGoogle Scholar
  6. 6.
    Marrelec, G., Benali, H., Ciuciu, P., Poline, J.: Bayesian estimation of haemodynamic response function in functional MRI, CP617. In: Bayesian Inference and Maximum Entropy methods in Science and Engineering: 21st International Workshop, pp. 229–247 (2002)Google Scholar
  7. 7.
    Bretthorst, G.L.: Bayesian interpolation and deconvolution. The U.S Army Missile Command, Tech. Rep. CR-RD-AS-92-4 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pradeep Kumar Shetty
    • 1
  1. 1.Honeywell Technology Solutions LabBangaloreIndia

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