scholarly journals The reassigned pseudo Wigner–Ville transform in electrochemical noise analysis

2019 ◽  
Vol 21 (44) ◽  
pp. 24361-24372 ◽  
Author(s):  
Ivana Jevremovic ◽  
Andreas Erbe
Keyword(s):  
Signal Processing ◽  
Time Evolution ◽  
Electrochemical Noise ◽  
Noise Analysis ◽  
Time Frequency ◽  
Noise Data ◽  
Frequency Components

Several different time–frequency transforms from signal processing were used to analyze electrochemical noise data to determine frequency components contained within the noise record and their time evolution.

THE USE OF ELECTROCHEMICAL NOISE ANALYSIS ON CORRODING SYSTEMS

2004 ◽  
Vol 04 (03) ◽  
pp. R39-R55 ◽  
Author(s):  
G. MONTESPERELLI ◽  
G. GUSMANO
Keyword(s):  
Electrochemical Noise ◽  
Quantitative Estimation ◽  
Noise Analysis ◽  
Electrochemical Techniques ◽  
Sampling Interval ◽  
Entropy Method ◽  
Corrosion Mechanism ◽  
Statistical Parameters ◽  
Noise Data ◽  
Sampling Duration

This paper gives an overview of the use of Electrochemical Noise (EN) for corrosion studying and monitoring. Since the quality and reliability of noise data are affected by a number of acquisition parameters, such as sampling interval, sampling duration, D.C. trend and instrumental noise, some experimental and practical aspects were discussed. The use of statistical parameters such as standard deviation, Pit Index and/or Localization Index and Noise Resistance to analyze noise data of corroding systems were examined. Many experimental applications of Electrochemical Noise Measurements on different metals and alloys were given. EN data have been compared with traditional electrochemical techniques. EN allowed to characterize the corrosion behavior of samples giving in some cases good quantitative estimation. The transposition of current and potential noise acquisition in the frequency domain (by Fast Fourier Transform and/or Maximum Entropy Method), gave further information on corrosion mechanism and in particular permitted to identify the type of corrosion. Finally the use of Discriminant Analysis permitted to deduce the best sampling frequency and sampling duration for EN acquisition, able to discriminate between two different situations.

Novel time–frequency characterization of electrochemical noise data in corrosion studies using Hilbert spectra

2013 ◽  
Vol 66 ◽  
pp. 97-110 ◽  
Author(s):  
A.M. Homborg ◽  
E.P.M. van Westing ◽  
T. Tinga ◽  
X. Zhang ◽  
P.J. Oonincx ◽  
...  
Keyword(s):  
Electrochemical Noise ◽  
Time Frequency ◽  
Corrosion Studies ◽  
Noise Data

A SYSTEM IDENTIFICATION APPROACH TO ESTIMATING COMPLEX IMPEDANCE SPECTRA FROM ELECTROCHEMICAL NOISE DATA

2003 ◽  
Vol 06 (04) ◽  
pp. 575-597
Author(s):  
GERARDO MIRAMONTES DE LEÓN ◽  
DAVID C. FARDEN ◽  
LYLE E. McBRIDE
Keyword(s):  
System Identification ◽  
Electrochemical Noise ◽  
Noise Analysis ◽  
Complex Impedance ◽  
Electrode Impedance ◽  
Power Spectral ◽  
Noise Data ◽  
Perfect Symmetry ◽  
Identification Approach ◽  
Identification Techniques

A system identification approach for the analysis of electrochemical noise data is proposed. The most common techniques, used by many investigators, are based on: (i) the ratio of sample standard deviations, which gives no information about the frequency dependence of the electrode impedance, or (ii) Power Spectral Density estimates, which deliver the modulus of the spectrum with large variations at the lowest frequencies. Phase is, almost invariably, not included. In this work, the electrochemical cell is modeled by an input-output model. With the application of system identification techniques, it is possible to identify values of the parameters of the system model. It is shown that this approach delivers a description of the system under study with: smooth electrode impedance curves, and magnitude and phase information. Some results obtained with the most common electrochemical noise analysis techniques are presented for comparison with the proposed approach. A theoretical limitation of the proposed approach appears if a perfect symmetry between both electrodes is considered.

Evaluation of microbially influenced corrosion with electrochemical noise analysis and signal processing

2007 ◽  
Vol 52 (19) ◽  
pp. 5795-5807 ◽  
Author(s):  
Nikita Zaveri ◽  
Rongtao Sun ◽  
Nephi Zufelt ◽  
Anhong Zhou ◽  
YangQuan Chen
Keyword(s):  
Signal Processing ◽  
Electrochemical Noise ◽  
Noise Analysis ◽  
Microbially Influenced Corrosion

scholarly journals Electrochemical noise analysis to obtain the Rsn value via FFT using Excel

2020 ◽  
Vol 45 (4) ◽  
pp. 57-70
Author(s):  
Sidineia Barrozo ◽  
Riberto Nunes Peres ◽  
Marcus José Witzler ◽  
Assis Vicente Benedetti ◽  
Cecílio Sadao Fugivara
Keyword(s):  
Electrochemical Noise ◽  
Rapid Determination ◽  
Noise Analysis ◽  
External Signal ◽  
Noise Resistance ◽  
Microsoft Excel ◽  
Step Procedure ◽  
Noise Data ◽  
Excellent Tool

Electrochemical noise (EN) measurements are based on the fluctuations of the electrochemical potential and the current that occur during, for example, a corrosion process without an external signal perturbation. EN analysis (ENA) allows assessment of the type of corrosion and rapid determination of the corrosion rate. Microsoft Excel®, an inexpensive and readily available software package, is an excellent tool for performing repetitive calculations, with automation that saves time for the users. It is a useful tool for the analysis of EN data using fast Fourier transform (FFT), a process that is often made repeatedly and, if not automated, is quite laborious. This work presents a step-by-step procedure using Excel to perform these calculations, automating the process of obtaining the spectral electrochemical noise resistance, . This routine was used to analyze experimental potential and current noise data recorded for chalcopyrite. The results were comparable to those obtained for the same set of experimental data using Origin® software.

Time–frequency methods for trend removal in electrochemical noise data

2012 ◽  
Vol 70 ◽  
pp. 199-209 ◽  
Author(s):  
A.M. Homborg ◽  
T. Tinga ◽  
X. Zhang ◽  
E.P.M. van Westing ◽  
P.J. Oonincx ◽  
...  
Keyword(s):  
Electrochemical Noise ◽  
Time Frequency ◽  
Noise Data ◽  
Trend Removal

Guided Waves in Steel Rails – Experimental Works and Wavelet Signal Processing

2007 ◽  
Vol 347 ◽  
pp. 115-120
Author(s):  
Magdalena Rucka ◽  
Krzysztof Wilde
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Guided Waves ◽  
Flaw Detection ◽  
Transverse Waves ◽  
Dispersive Waves ◽  
Time Frequency ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper presents experimental study on dispersive waves propagation in steel rails. The propagation of longitudinal and transverse waves was generated by an impulse hammer and measured in three points. Wavelet transform (WT) and short time Fourier transform (STFT) were applied to analyze the time signals. Analysis of signal by STFT does not provide a proper timefrequency representation due to a fixed size window. The wavelet transform can effectively identify the time-frequency components in waves. The wavelet signal processing of the experimental wave propagation signals is intended to be used for rail flaw detection.

OPTIMAL WAVELET CHOICE IN ELECTROCHEMICAL EXPERIMENTS

2006 ◽  
Vol 06 (02) ◽  
pp. L215-L225 ◽  
Author(s):  
K. DAROWICKI ◽  
A. ZIELIŃSKI
Keyword(s):  
Signal Processing ◽  
Wavelet Analysis ◽  
Digital Signal Processing ◽  
Electrochemical Noise ◽  
Noise Analysis ◽  
Digital Signal ◽  
Comparison Of Results ◽  
Processing Techniques ◽  
Signal Processing Techniques

In the recent years we witness great interest in merging electrochemical experiments with modern digital signal processing techniques. Wavelet analysis seems to be one of the most promising approaches. In the case of wavelets there are various families of analyzing functions which can be selected to suit given experiment demands. In the paper the authors present comparison of results of utilization of different wavelets in electrochemical noise analysis.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

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