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An Undecimated Wavelet Transform Based Enhancement, Statistical Feature Extraction and Detection-Classification of PD Signals

Shetty, Pradeep Kumar and Ramu, TS (2004) An Undecimated Wavelet Transform Based Enhancement, Statistical Feature Extraction and Detection-Classification of PD Signals. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), 17-21 May, Quebec,Canada, Vol.5, 401-404.

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Abstract

Authors 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 frequency characteristics as PD pulse. Also, the occurrence of PI is random like PD pulses. In this paper we provide techniques to de-noise, detect, estimate and classify the PD signal in a statistical perspective. To avoid aliasing due to interference of high frequency noise, PD signals are generally digitized in much higher sampling rates (in terms of tens of MHz), than actually required. A multi-resolution analysis based technique is incorporated to discard the huge amount of redundant data in acquired signal. A scale dependent MMSE based estimator is implemented in undecimated wavelet transform (UDWT) domain to enhance the noisy signal, due to its inherent advantages offered in the analysis of PD signal. The probability density function of the enhanced signal is derived using probabilistic principal component analysis (PPCA) in which PD/PI pulses are modeled as mean of the distribution. The parameters of the pulses are estimated using maximum aposteriroi probability (MAP) based technique. A statistical test known as generalized log likelihood ratio test (GLRT) was incorporated to ensure the existence of the pulse. The decision as to whether a pulse is a noise or a desired signal has been made based on a weighted-nearest neighbor methodology.

Item Type: Conference Paper
Additional Information: ©1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > High Voltage Engineering (merged with EE)
Date Deposited: 14 Dec 2005
Last Modified: 19 Sep 2010 04:22
URI: http://eprints.iisc.ernet.in/id/eprint/4488

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