scholarly journals Wavelet Transform Modulus Maxima and Holder Exponents Combined with Transient Detection for the Differentiation of Pitting Corrosion Using Electrochemical Noise

CORROSION ◽  
10.5006/2788 ◽  
2018 ◽  
Vol 74 (9) ◽  
pp. 1001-1010 ◽  
Author(s):  
A.M. Homborg ◽  
P.J. Oonincx ◽  
J.M.C. Mol
Keyword(s):  
Wavelet Transform ◽  
Pitting Corrosion ◽  
Electrochemical Noise ◽  
Localized Corrosion ◽  
General Corrosion ◽  
Hölder Exponent ◽  
Transient Detection ◽  
Time Frequency ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

A potentially powerful tool to detect and classify corrosion mechanisms is the analysis of electrochemical noise (EN). Data analysis in the time-frequency domain using, e.g., continuous wavelet transform (CWT) allows the extraction of localized frequency information, providing information on the type of corrosion, i.e., uniform or localized corrosion, from the EN signal. The CWT provides the opportunity to analyze changes in frequency behavior of EN signals over time. In the presence of transients generated by pitting corrosion that occur only during short instants of time, this is an important property. This paper introduces the combination of automated transient detection with wavelet transform modulus maxima (WTMM) and the Holder exponent. WTMM enhances the determination of transient frequencies by indicating the ridges of a CWT spectrum. The Holder exponent, a measure of singularity of an EN signal, provides a single parameter discrimination tool based on WTMM and serves to differentiate between general corrosion and two types of pitting corrosion of stainless steel Type 304 exposed to aqueous HCl solutions of different concentrations and as such at different pH values.

WEAK LFM SIGNAL DECTECTION BASED ON WAVELET TRANSFORM MODULUS MAXIMA DENOISING AND OTHER TECHNIQUES

2010 ◽  
Vol 08 (02) ◽  
pp. 313-326 ◽  
Author(s):  
BO LE ◽  
ZHONG LIU ◽  
TIANXIANG GU
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Correlation Coefficients ◽  
Time Frequency ◽  
Auto Correlation ◽  
Wavelet Transform Modulus Maxima ◽  
Reconstructed Signal ◽  
Modulus Maxima ◽  
Domain Information

A new method for detecting weak linear frequency modulated (LFM) pulse signals buried in additive white Gaussian noise (AWGN) is presented in this paper. The method is based on the features of wavelet transform modulus maxima (WTMM) denoising and auto-correlation filtering theory. Firstly, the frequency-domain information is extracted by auto-correlation matched filtering, and is used to deduce the optimal wavelet decomposition scales. Secondly, let the signal modulus dominate on the biggest scale after the optimal scales decomposition, then keeping the signal modulus and removing the noise modulus at each scale are performed by utilizing the different propagation properties of signal and noise wavelet modulus maxima across the scales. Finally, a reconstructed signal is obtained from the reserved signal modulus with an improved signal-to-noise ratio (SNR), and is used for time-domain information extraction. At the same time, wavelet denoising depends on selecting an optimum wavelet that matches well the shape of the signal. The cross correlation coefficients between signal and db wavelets are calculated and the optimal wavelet to analysis the LFM signal is selected. Simulations show that the method can extract time-frequency information of LFM signal when SNR ≤ -6 dB .

scholarly journals DETERMINING LOCAL SINGULARITY STRENGTHS AND THEIR SPECTRA WITH THE WAVELET TRANSFORM

Fractals ◽  
2000 ◽  
Vol 08 (02) ◽  
pp. 163-179 ◽  
Author(s):  
ZBIGNIEW R. STRUZIK
Keyword(s):  
Wavelet Transform ◽  
Multifractal Analysis ◽  
Real Life ◽  
Life Time ◽  
Hölder Exponent ◽  
Multifractal Formalism ◽  
Wavelet Transform Modulus Maxima ◽  
Local Singularity ◽  
Modulus Maxima ◽  
Multiplicative Cascade

We present a robust method of estimating the effective strength of singularities (the effective Hölder exponent) locally at an arbitrary resolution. The method is motivated by the multiplicative cascade paradigm, and implemented on the hierarchy of singularities revealed with the wavelet transform modulus maxima (WTMM) tree. In addition, we illustrate the direct estimation of the scaling spectrum of the effective singularity strength, and we link it to the established partition function-based multifractal formalism. We motivate both the local and the global multifractal analysis by showing examples of computer-generated and real-life time series.

CONTINUOUS WAVELET TRANSFORM MODULUS MAXIMA ANALYSIS OF THE ELECTROCARDIOGRAM: BEAT CHARACTERISATION AND BEAT-TO-BEAT MEASUREMENT

2005 ◽  
Vol 03 (01) ◽  
pp. 19-42 ◽  
Author(s):  
I. ROMERO LEGARRETA ◽  
P. S. ADDISON ◽  
M. J. REED ◽  
N. GRUBB ◽  
G. R. CLEGG ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Positive Predictive Value ◽  
Continuous Wavelet Transform ◽  
Predictive Value ◽  
Royal Infirmary ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

The problem of automatic beat recognition in the ECG is tackled using continuous wavelet transform modulus maxima (CWTMM). Features within a variety of ECG signals can be shown to correspond to various morphologies in the CWTMM domain. This domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of this enhanced time-frequency analysis technique for ECG signals, an R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.70% and a positive predictive value of 99.68%).

Development of Time-Frequency Analysis in Electrochemical Noise for Detection of Pitting Corrosion

CORROSION ◽  
10.5006/2900 ◽  
2018 ◽  
Vol 75 (2) ◽  
pp. 183-191 ◽  
Author(s):  
Mohammad Nazarnezhad Bajestani ◽  
Jaber Neshati ◽  
Mohammad Hossein Siadati
Keyword(s):  
Frequency Analysis ◽  
Pitting Corrosion ◽  
Electrochemical Noise ◽  
Time Frequency Analysis ◽  
Time Frequency

Study for Detection Algorithm of QRS Complex in ECG Signal

2013 ◽  
Vol 765-767 ◽  
pp. 2105-2108
Author(s):  
Xu Wen Li ◽  
Bi Wei Zhang ◽  
Qiang Wu
Keyword(s):  
Wavelet Transform ◽  
Detection Algorithm ◽  
Automatic Analysis ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Good Precision ◽  
Qrs Complex ◽  
Noise Interference ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

In ECG signals accurate detection to the position of QRS complex is a key to automatic analysis and diagnosis system. And its premise is that effectively remove all kinds of noise interference in ECG signal. Here, a method of detecting QRS based on EMD and wavelet transform was presented which is aim to improve the anti-noise performance of the detection algorithm. It is combined EMD with the theory of singularity detecting based on wavelet transform modulus maxima method. It has the high detection accuracy and good precision that can give an effective way to the automatic analysis for ECG signal.

An Improved Denoising Method in RDTS Based on Wavelet Transform Modulus Maxima

2015 ◽  
Vol 15 (2) ◽  
pp. 1061-1067 ◽  
Author(s):  
Zongliang Wang ◽  
Guangping Lv ◽  
Jun Chang ◽  
Sasa Zhang ◽  
Sha Luo ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Denoising Method ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima
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