Abstract
We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Satish, L., Nazneen, B.: Wavelet denoising of PD signals buried in excessive noise and interference. IEEE Transaction on DEI 10(2), 354–367 (2003)
Tipping, M.E., Bishop, C.M.: A hierarchical latent variable model for data visualization. IEEE trans. PAMI 20(3), 25–35, 281–293 (1998)
Hayes, M.H.: Statistical Digital Signal Processing and Modelling, ch. 8, pp. 445–447. John Wiley and Sons inc., West Sussex (1996)
Flandrin, P.: Wavelet analysis and synthesis of fractional Brownian motion. IEEE transaction on Information Theory 38(2), 910–917 (1992)
Wornell, G.: Signal Processing with Fractals: A Wavelet Based Approach, ch. 3, pp. 30–46. Prentice Hall PTR, Newjersy (1996)
Wornell, G.W.: A Karhunen-Loeve-like expansion for 1/f processes via wavelets. IEEE, Trans. Inform. Theory 36, 859–861 (1990)
Wornell, G.W.: A Karhunen-Loeve-like expansion for 1/f processes via wavelets. IEEE. Trans. Inform. Theory 36, 859–861 (1990)
Stone, G.C.: Practical techniques to measure PD in operating equipment. In: Proc. 3rd Int. Conf. on Properties and Application of Dielectric Materials, Tokyo, Japan, pp. 1–17 (1991)
Kay, S.M.: Fundamentals of Statistical Signal Processing-Estimation Theory, ch. 7,10,11,12, pp. 157–214, 309–415. Prentice Hall PTR, Newjersy (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shetty, P.K. (2004). A Long Memory Process Based Parametric Modeling and Recognition of PD Signal. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_121
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive