TY - GEN
T1 - An undecimated wavelet transform based denoising, PPCA based pulse modeling and detection-classification of PD signals
AU - Shetty, Pradeep Kumar
AU - Ramu, T. S.
PY - 2004
Y1 - 2004
N2 - Authors Address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottle-neck 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.
AB - Authors Address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottle-neck 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.
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U2 - 10.1109/ICPR.2004.1333911
DO - 10.1109/ICPR.2004.1333911
M3 - Conference contribution
AN - SCOPUS:10044288083
SN - 0769521282
T3 - Proceedings - International Conference on Pattern Recognition
SP - 873
EP - 876
BT - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
A2 - Kittler, J.
A2 - Petrou, M.
A2 - Nixon, M.
T2 - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Y2 - 23 August 2004 through 26 August 2004
ER -