Denoising of partial discharge signals in wavelet packets domain

Chang, Chin Sung; Jin, Jaehyeok; Kumar, Sanjeev; Su, Qi; Hoshino, T.; Hanai, Masahiro; Kobayashi, N.

doi:10.1049/ip-smt:20041315

Cited by 44 publications

(39 citation statements)

References 11 publications

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“…This paper will not cover the details of how signal processing techniques work, but the authors would like to note that de-noising is processing it. Wavelet transform techniques [75][76][77] have been successfully applied to process non-stationary signals in the past, such as the vibration signal of gear boxes [78], bearings [79] and other rotary mechanical systems [80]. Moreover, from the shape of the output signal (see Figure 1), the electrostatic monitoring signal can be observed to be similar to the electrocardiography (ECG) signal [81].…”

Section: Signal Processingmentioning

confidence: 99%

A review of electrostatic monitoring technology: The state of the art and future research directions

Zhang

Hou

Atkin

2017

Progress in Aerospace Sciences

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Electrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for system health management. It encompasses three key research areas: sensor technology; signal detection, processing and feature extraction; and verification experimentation. It has received considerable recent attention for condition monitoring due to its ability to provide warning information and non-obstructive measurements on-line. A number of papers in recent years have covered specific aspects of the technology, including sensor design optimization, sensor characteristic analysis, signal de-noising and practical applications of the technology. This paper provides a review of the recent research and of the development of electrostatic monitoring technology, with a primary emphasis on its application for the aero-engine gas path. The paper also presents a summary of some of the current applications of electrostatic monitoring technology in other industries, before concluding with a brief discussion of the current research situation and possible future challenges and research gaps in this field. The aim of this paper is to promote further research into this promising technology by increasing awareness of both the potential benefits of the technology and the current research gaps.

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Section: Signal Processingmentioning

confidence: 99%

A review of electrostatic monitoring technology: The state of the art and future research directions

Zhang

Hou

Atkin

2017

Progress in Aerospace Sciences

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“…We started from a set of candidate wavelet families already used by other authors [12,13,15,17] such as Daubechies (db), Symlets (sym), Biorthogonal (bio), Coiflets (coif); in particular, by taking into account wavelets of different orders, the performances of a total number of 60 wavelets were compared.…”

Section: Wavelet Selectionmentioning

confidence: 99%

“…More recently Macedo et al [18] used the cross correlation factor as a unique performance parameter for the choice of an appropriate mother-wavelet, while in [17] Chang et al put in evidence that the possible reason for bad performances of the denoising techniques could be ascribed to a limited set of training signals or to an inadequate selection of the performing parameters.…”

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confidence: 99%

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Petrarca¹,

Lupo²

2014

PIER B

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Abstract-Partial Discharge (PD) measurements may be affected by external noise and disturbances of various natures such as interference from broadcasting stations, stochastic noise, pulses from power electronics, etc. Extracting PD pulses from such a noisy environment is therefore a crucial issue. This paper presents a wavelet based technique for automatic noise rejection. The core of the paper is the use of an improved methodological approach for the selection of a suitable wavelet, which aims at summing up the benefits and overcoming some limitations of previous techniques. Firstly, a very wide set of training signals is used for the identification of the decomposition level and for the calculation of suitable performance parameters that identify each wavelet; then a Performance Fingerprint is introduced in order to summarize the ability of a specific wavelet to reconstruct a partial discharge waveform, and a distance criterion is used for the selection of the most suitable wavelet. Afterwards, useful information is collected for the reconstruction of the PD signal, and finally, results on the application of the algorithm for a set of numerical and experimental signals are presented.

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“…As the intense electromagnetic interferences are surrounded by the power equipments, such as generator units, power transformers [3], gas insulated lines (GIL)/GIS, and cables [4], the weak PD signals are buried in various noises or interferences. WT [2,[5][6][7] and wavelet packet [3][4] have obtained widespread application, especially for complex wavelet transform (CWT) proposed in the literature [1,[8][9][10] has not only the same mathematical principle of processing the non-stationary signal, but also it has better denoising effect.…”

Section: Introductionmentioning

confidence: 99%

Investigation of suppressing PD white noise by compound information of complex wavelet transform

Tang

Zhong-rong

Sun

2008

Int Trans Elec Energy Syst

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SUMMARYAccording to the distinct properties between the complex wavelet coefficients of partial discharge (PD) signals and white noises, this paper presents the specific compound information (CI) of WTRI n series for complex wavelet transform (CWT), which are utilized to suppress white noises. Through the simulation case, the influence of n-index value on denoising effect is discussed and the optimal CI of corresponding to different PD waveforms is analyzed by the simulation investigation. The parameters of variable tendency parameter (VTP), normalized correlation coefficient (NCC), and signal-to-noise ratio (SNR) are defined to synthetically evaluate the denoising effect. The db4 complex wavelet is constructed to suppress the white noise in ultra high frequency (UHF) PD detection system. The results indicate that the denoising effect of the CI for CWT is better than the simple information (SI) for CWT and the CI of WTRI n series have the optimal denoising effect to suppress white noise for UHF PD signals.

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Denoising of partial discharge signals in wavelet packets domain

Cited by 44 publications

References 11 publications

A review of electrostatic monitoring technology: The state of the art and future research directions

A review of electrostatic monitoring technology: The state of the art and future research directions

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Investigation of suppressing PD white noise by compound information of complex wavelet transform

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