Fractal characteristic analysis of electrochemical noise with wavelet transform

In the study, the fractal parameters were introduced to describe the irregular characteristic of electrochemical noise, which were calculated with wavelet transform to overcome some defects of other methods. Compared with other fractal parameters, wavelet standard deviation (STD) exponent (Hws) had smaller deviation and could describe the fractal characteristics of electrochemical noises in a wide range, therefore Hws was chosen to evaluate general and local irregularity of potential signals generated from 7075-T76 aluminum alloy in 3.5% NaCl solution without and with different inhibitors respectively. The results showed that the smaller general Hws value, the more frequently potential fluctuated on the electrode surface, and the less effect of inhibitor in the solution. The difference between general Hws and 1 reflected the condition of surface passivity. Local Hws could effectively extract the local feature of electrochemical noise and quantitatively describe the change of its fractal characteristic with time, which was a promising index to analyze electrochemical noise in practice.     

Electrochemical noise analysis of localized corrosion by wavelet transform

Electrochemical noise (EN) was measured during pitting and stress corrosion tests in NaCl solutions for type 403 stainless steel tempered at 390 °C for 2 h. The EN data were analyzed mainly by wavelet transform in terms of the energy contribution of smooth crystal and detail crystals in three frequency regions. In the early stage of the pitting tests, general corrosion occurs, and contribution of smooth crystal to the total energy was greater than that of low frequency detail crystal, but less with the development of meta-stable pitting. A similar trend was observed in the early stage of stress corrosion cracking (SCC) tests. The contribution of low frequency detail crystals to the total energy decreased as stable pits were formed, and it was less than the energy contribution of smooth crystal when crack propagation occurred in the later stage of SCC. In addition, the contribution of energy of high frequency detail crystal is shown to be indicative of the occurrence of plastic deformation. These results demonstrate that the onset of detailed processes in pitting and SCC can be sensitively detected by wavelet analysis in terms of the energy contribution of smooth crystal and low and high frequency detail crystals, but not with that based on the change in position of peak in energy contribution of detail crystals.     

Application of the wavelet transform in mechanical spectroscopy and in Barkhausen noise analysis

In this work, the wavelet transform and the Fast Fourier Transform FFT are introduced to analyze the quality of the strain response signal in order to improve the accuracy of computation of both the logarithmic decrement and the mechanical loss angle. The wavelet transform of the strain response signal yields a three-dimensional representation (the time-scale joint representation), i.e. space (time), frequency (scale) and amplitude. To emphasize that not only time but also frequency content of the strain response signal collected from a mechanical spectrometer is identified, the author suggests that the time-scale joint representation of the strain response signal should be called ‘Identified Strain Response Signal — ISRS’. The wavelet transform was shown to be an excellent tool to test the quality of the strain and/or of the stress signal in a mechanical spectrometer working in a resonant or subresonant mode. A new approach to the non-stationary Barkhausen noise (BN) signal based on the wavelet transform is also presented. The BN level is usually expressed with the magnetoelastic parameter MP which is a relative number proportional to the root mean square level of the BN. It was found that the MP value is not a reliable parameter for the identification of stress concentrations in ferromagnetic materials. Indeed, a stress concentration resulting from internal stresses can be successfully revealed by the time-scale joint representation of the BN signal. The author suggests that the wavelet transform of the non-stationary RN signal should be called the ‘Identified Barkhausen Noise — IBN’. This approach produces a physically reliable relationship between the IBN and either the level of internal stresses or fine variations in the microstructure.     

Use of the wavelet method for analyzing electrochemical noise data

This research paper shows the application of the mathematical tool “wavelet's” to be used in the interpretation and analysis of electrochemical noise signals. The Fast Fourier Transformation (FFT) is one of the most commonly used techniques for analyzing electrochemical noise. But the application of this methodology to the analysis of stochastic signals does not allow their interpretation in a suitable way. The wavelet's methodology carries out the analysis by means of the construction of finite base functions. In several studies, it has been found out that the function that is closest to the electrochemical oscillations is the one called “coiflets,” which presents a similar geometry to what it is observed when an ionic–electronic interchange occurs during an electrochemical interphase. The analysis that appears here was implemented using the toolbox “LabView?.”     

GPR signal de-noising by discrete wavelet transform

Ground penetrating radar (GPR) is a non-destructive investigation tool used for several applications related to civil infrastructures; including buried objects detection and structural condition evaluation. Although GPR can be effectively used to survey structures, signal analysis can be sometimes challenging. The GPR signals can be easily corrupted by noise because the GPR receiver has usually an ultra-wide bandwidth (UWB). The noise collected by the system can easily mask relatively weak reflections resulting from the inhomogeneities within the surveyed structure; especially when they are at a relatively deep location. This paper presents the use of discrete wavelet transform (DWT) to de-noise the GPR signals. Various mother wavelets were used in this study to de-noise experimental GPR signals collected from flexible pavements. The performance of wavelet de-noising was evaluated by computing the signal-to-noise ratio (SNR) and the normalized root-mean-square error (NRMSE) after de-noising. The study found that wavelet de-noising approach outperforms traditional frequency filters such as the elliptic filter. At the same level of decomposition, the Daubechies order 6 and Symlet order 6 outperform the Haar and Biorthogonal mother wavelets when de-noising GPR signals by soft thresholding.     

Morphological characterisation of engineered surfaces by wavelet transform

This work introduces a wavelet transform technique based on the detection of the multi-scale view of the components of a signal . The main advantages of wavelet transform over the existing signal processing techniques are its space-frequency localization and multi-scale analysis of roughness and waviness motifs. After an extensive review of the mathematical and signal processing fundamentals of the wavelet technique, numerical implementations are carried out to explore the potential applications of wavelet transform and its inverse transform multi-scale analysis of roughness and local morphological characterization by the detection of the roughness singularities and 21) motif size of an engineered surface.     

Evaluation of pile defects using complex continuous wavelet transform analysis

In recent years, the technique of wavelet transform has been applied widely in signal processing in different fields, including non-destructive testing of pile foundations. However, it was used mostly in signal filtering and the analysis of time-frequency diagram. This paper successfully utilized complex continuous wavelet transform to determine pile length and locations of defects on pile foundations by analyzing the time-frequency-phase angle diagram in different frequency band. Six piles with different types of defects were installed and tested to verify the proposed approach in this study. The results shows that complex continuous wavelet transform not only is able to provide high resolution results in different frequency bands, which is similar to that of continuous wavelet transform, but also simplifies the identification of the reflection of defects using 3D phase spectrogram. The location of defects can then be easily determined using phase diagram under the corresponding specific frequency.     

evaluation of engineering surfaces using a combined fractal modeling and wavelet analysis method

A new approach based on the combination of wavelet and fractal theories is proposed. The purpose is to provide a mechanism to evaluate the characteristics of engineering surfaces more accurately and comprehensively. The wavelet transformation models and the fractal representation of engineering surfaces are presented and the combination of the wavelet models and the fractal representation is investigated. Using the proposed approach, the experimental samples of the workpiece surface obtained through grinding are examined. The results demonstrate that the proposed approach is accurate and comprehensive.     

Screening of new corrosion inhibitors via electrochemical noise analysis

The inhibition efficiency of a series of organic compounds against corrosion of magnesium alloy AZ91 in a 50 wt.‐% aqueous solutions of ethylene glycol has been studied using polarisation curves and electrochemical noise (ECN) measurements. The comparative analysis of the results obtained has demonstrated unambiguously that the method of ECN is an effective tool for screening of new corrosion inhibitors which offers some advantages comparing to the common electrochemical techniques. The very fast measuring rate enables to instantaneously register and monitor the rate of the corrosion process and changes in its mechanism in presence of the organic additives. The measurements are performed at freely corroding “identical” electrodes without perturbation from the external signals, which could lead (as in the polarisation measurements), to changes in the surface structure, the adsorption of the inhibitor etc. The statistical analysis of the noise data (in terms of standard deviations) in the time domain shows that the noise intensity of the system correlates well with the intensity of the corrosion process. The total amount of the charges exchanged during the tests is a quantitative measure for the inhibiting effect of the substances under study, while the spectral analysis of the noise in the frequency domain can be used to characterise the type of the corrosion attack.     

Time–energy density analysis based on wavelet transform

Energy is an important physical variable in signal analysis. The distribution of energy with the change of time and frequency can show the characteristics of a signal. A time–energy density analysis approach based on wavelet transform is proposed in this paper. This method can analyze the energy distribution of signal with the change of time in different frequency bands. Simulation and practical application of the proposed method to roller bearing with faults show that the time–energy density analysis approach can extract the fault characteristics from vibration signal efficiently.     

Monitoring of vitreous enamel degradation by electrochemical noise

Vitreous coatings are widely used for protecting steel at temperatures up to 600 °C in applications where high oxidation- and wear-resistance are required. However, the action of alkaline chemical agents such as household cleaning products can cause the devitrification and spalling of these coatings, with the consequent loss of their protective properties. In such cases, corrosion of the substrate is accelerated due to the heat treatment applied during the manufacturing process of the coating — tempering or curing. The present work characterizes the initial stages of this deterioration using the electrochemical noise technique (EN). When coatings are undamaged the current noise is very low, with current intensity values of the order of picoamperes. However, when defects appear as a result of chemical attack, the systems electrical activity changes considerably and there is an increase in both current and potential noise. This work also establishes the loss of the coatings protective nature as a function of spectral noise resistance, R_sn, finding good concordance with values obtained by other more conventional techniques such as electrochemical impedance.     

Multiscale characterizations of painted surface appearance by continuous wavelet transform

The appearance of paint is an important factor in the overall product quality of engineered surfaces, especially in the automotive industry. This paper introduces a multiscale characterization technique of surface topography from roughness to waviness. It uses continuous wavelet transform to correlate the characteristic scale of the surface texture to the paint's appearance. The results show good correlation between the multiscale roughness spectrum and standard distinctness of image (DOI) measurement. After an extensive review of the mathematical and numerical implementations, the multiscale attenuation function of the painting process (MAF) is defined to study the effect of the successive layers of paint coat and the painting orientation modes (horizontal, oblique and vertical).     

Investigation of the initial stage of crevice corrosion on Al99.5 by electrochemical noise analysis

The electrochemical noise analysis (ENA) is a powerful tool to investigate the initial state of local corrosion processes as pitting corrosion or crevice corrosion on aluminium alloys. The focus of this work lays on Al99.5 (AA1050). In spite of the good corrosion protection and the very low passive current density the noise activity in the early state of corrosion can be very well observed. ENA was performed by using current noise measurement under potentiostatic control. The effect of chromate inhibitors on the noise activity of the initial state of corrosion on Al99.5 is surprisingly negligible. This phenomenon is discussed in this paper.     

Monitoring batch treatment inhibitor performance continuously using electrochemical noise analysis

Abstract

Electrochemical noise analysis (ENA) was used to monitor continuously the formation and deterioration processes of a commercial batch treatment inhibitor film of the type used for protecting against CO₂ corrosion in oilfields; ENA was shown to be able to follow effectively the formation and deterioration processes of batch treatment inhibitor films. As an inhibitor film formed, the current noise amplitude decreased rapidly and the noise resistance R_n, which is deducible from the voltage and current noise records, was found to increase sharply. Conversely, as the inhibitor film deteriorated, the current noise amplitude increased rapidly and R_n decreased rapidly. In the corrosion inhibition system studied, the noise resistance was confirmed to be similar to the linear polarisation resistance. Based on the calculation of R_n on a continuous basis, a technique is proposed to study fast corrosion processes.     

Modeling of electrochemical noise transients

Starting from the elementary corrosion reactions on a passive metal surface a model is developed for the origin of electrochemical noise. The ion currents flowing at the electrochemical reactions are described by the electrons remaining in the metal and then interpreted as a measuring signal. The measurable sum current is composed of the superimposition of the electron currents of the partial reactions. Due to the inhomogeneity of the metal surface, local metal dissolutions take place in preferential areas until the area is consumed or repassivated after short times. The short‐time surplus of free electrons leads for every metal dissolution event to a current transient, which itself consists of the partial currents of the elementary anodic and cathodic reactions. Due to the different reaction speeds, there are temporary charge disequilibria and thus measurable noise transients. In model calculations the influence of the diffusion of the oxygen on the sum signal as well as the variation of the anodic current is shown. Furthermore, the results show that a measurable noise signal can only arise under certain conditions. This signal arisen directly at the metal surface is not, however, ascertainable without further amplification. In the measurement amplifiers filter stages play an important role for signal conditioning. The influence of the measuring technique is shown at the filtration of a simulated test signal in different frequency domains. It is discussed how the signal distortion affects the detectability of the partial reactions described at the beginning and in which way the noise signal is estimated. Indications for the layout of suitable measuring parameters are given.     

使用小波分析工业电解中铜阳极行为的电化学噪声(英文)

在温度为65℃,含有2mg/L骨胶和4mg/L硫脲的工业电解液中,测量Cu-Pb合金阳极溶解和钝化的电化学噪声数据。通过小波变换技术分析计时电位图,将噪声数据分为活跃区,预钝化区和钝化区。实验结果表明:在恒定电流密度为215mA/cm2的条件下,活跃区和预钝化区出现点状局部腐蚀,而在钝化区则出现均匀腐蚀和氧析出。     

Interpretation of current noise generation by a simple model

The corrosion process of low carbon steel in natural aerated sulphuric acid was examined by electrochemical noise. Noise was interpreted in the time domain in terms of current fluctuations and amplitude distribution. A simple electrochemical model was used to analyse the electrochemical state of steel surface. Stochastic behaviour of the metal‐electrolyte interface in the model was based upon the assumption that the elementary fluctuation sources were related to fluxes of electrons that pass from a metal to electron‐acceptor ions in solution. The probability that in a certain time a number of successful electron transfers occur, obeyed the Gaussian distribution. At the corrosion potential, it was possible to estimate the corrosion current density (i_corr) from the number of successful electron transfers.     

Power signal separation in milling process based on wavelet transform and independent component analysis

Among many machining condition monitoring systems, a spindle motor power monitoring system is considered as one of the most popular systems for plant floor applications. However, in practice, power signals are mixed with many signal sources relevant to cutting tool, cutting conditions as well as components of a machine tool, which contaminate with each other in feature extraction processes and decrease the monitoring reliability. In this paper, modified blind sources separation (BSS) technique is used to separate those source signals in milling process. A single-channel BSS method based on wavelet transform and independent component analysis (ICA) is developed, and source signals related to a milling cutter and spindle are separated from a single-channel power signal. The experiments with different tool conditions illustrate that the separation strategy is robust and promising for cutting process monitoring.