基于电化学噪声研究模拟海洋大气环境下304不锈钢的点蚀行为

基于电化学噪声技术建立了不锈钢海洋大气点蚀监测系统,利用该系统对处于干湿循环环境下不锈钢的点蚀行为进行监测. 使用时域谱图、时域统计、频域谱图和散粒噪声理论等分析方法对采集到的电化学噪声数据进行处理分析,并结合动电位极化法,形貌分析法共同研究不锈钢的点蚀行为. 研究结果表明,304不锈钢在模拟海洋大气环境下的点蚀行为分为钝化、亚稳态点蚀和稳态点蚀三个阶段. 在钝化阶段,电位电流噪声信号出现少量的同步异向波动,腐蚀事件发生频率高,平均电量低;在亚稳态点蚀阶段,电位电流噪声信号出现大量的同步同向波动,腐蚀事件发生频率降低,平均电量上升,通过扫描电镜观察蚀点;在稳态点蚀阶段,电位电流噪声信号不仅存在大量的同步同向波动,还出现了同步异向波动,腐蚀事件发生频率较低,平均电量大幅度上升,通过扫描电镜观察到电极表面出现小而浅的蚀点. 而动电位极化法可以证实304不锈钢点蚀的发生. 两种分析方法所得结果具有较好的一致性,证明该监测系统很好地实现了对模拟海洋大气环境下304不锈钢点蚀行为的连续监测,并能判断点蚀的发生.     

一种基于二进小波变换的自适应滤波方法

根据信号和噪声经小波变换后在不同尺度上有不同的特征，将相邻尺度二进小波变换值的相关量进行归一化处理并与小波变换值比较来判断信号与噪声，以噪声在各尺度的方差作为终止迭代的标准，提出了一种基于二进小波变换小波域选择噪声的自适应滤波方法。研究了模拟信号的去噪过程、半峰宽和信噪比对去噪结果的影响，并对模拟含噪信号和含噪毛细管电泳信号去噪前后的结果进行了比较。实验结果表明：由于该方法具有良好的自适应性和显著的滤波效果，必将得到广泛的应用。     

毛细管电泳激光诱导荧光检测信号的小波滤噪

采用小波滤噪方法对毛细管电泳激光诱导荧光检测信号进行了处理，研究了小波变换中小波基的选择及噪声阈值的选择对滤噪的影响。结果表明，采用DB4小波基能有效消除毛细管电泳激光诱导荧光检测信号中存在的噪声，使信噪比得到较大改善。     

基于提升格式的小波变换用于处理化学信号

将提升haar小波变换应用于对不同类型含高噪声化学信号的处理,提出1种用于高噪声化学信号中滤除噪声的快速新方法——提升小波滤噪法,并使之与重叠峰分辨技术联用;以优选的小波分解层数对低信噪比的分析化学信号进行基于提升格式的小波变换处理,取得满意的结果;方法简单、快捷、准确、易行．运算速度是传统小波变换的一半,对高噪声化学信号的处理结果信噪比提高几百倍,峰位置相对误差小于1．5％;应用于氨基酸体系毛细管电泳检测信号的处理,有效降低了实验噪声的影响,分辨提取了难以察觉的信号．结果峰形变窄,峰高增加．大大提高了峰的分辨率．验证和显示了方法的可行性和优越性。     

因次分析法在电化学噪声分析中的应用

采用电化学噪声技术,研究了铝合金2024-T3在NaCl溶液中孔蚀过程的电化学噪声特 征.结果表明:电化学噪声的时域数据通过传统的FFT变换后得到的频域曲线SPD的特征参数(W 、、k和fc)均不能单独正确地刻画材料表面孔蚀的强度和趋势.基于因次分析法的基本原理 和对材料腐蚀过程影响因素的分析,从电化学噪声频域曲线SPD的特征参数出发,导出了能正 确表征材料表面孔蚀强度和趋势的两个参数SE和SG.其中,SE正比于孔蚀的强度IS,而SG的意 义仍在进一步的研究之中.     

二进小波变换极大模法用于分析化学信号的滤噪

实验数据的滤噪在分析化学领域中具有重要的意义。小波变换技术具有很强的信号分离能力，容易把随机噪声从信号中分离出来，从而提高信号的信噪比。本文使用滤噪方法不同于传统离散小波变换方法，而是通过引入二进小波变换和李氏指数的概念，根据噪声与有用信号的极大模截然不同的特征，实现信号滤噪。实验数据的仿真结果研究也证明该方法的可行性。     

基于小波变换的电位滴定法测定喷气燃料总酸值

在电位滴定法测定喷气燃料总酸值中采用小波变换法检测电位滴定终点。以二次样条小波为基函数对电位滴定曲线进行离散二进小波分解,在适当尺度上基本滤除曲线的干扰噪声,通过分析细节信号得到滴定曲线的突跃点,从而判定滴定终点。与指示剂法相比,本方法的测定结果具有相当的准确度和更高的精密度。本方法可快速测定喷气燃料的总酸值。     

电化学噪音数据小波分析法研究

电化学动力系统演化过程中 ,其状态参量 (如电极电位、电解池电流等 )的随机波动现象被称为“电化学噪音”(ECN) .小波变换是近年来数学与信号处理领域获得突破性进展的一种数据处理方法 .本文首次用连续小波变换的方法对混凝土中钢筋腐蚀产生的“电化学噪音”信号进行初步研究 ,通过将原始的时域信号变换至时_频相平面来观察其结构特征 ,并揭示出电化学腐蚀过程的混沌动力学行为 .     

一种有效提取微弱信号的新方法

根据小波变换能进行时频局部化分析的特性,本文提出一种用小波分段分析处理信号的方法。对任意长度的信号数据,在不增减信号点的情况下,提高分析信号的信噪比,有效地从信号中提取有用信息。将它应用于微弱信号检测,效果满意。     

烯胺阻锈剂对钢筋在模拟碳化混凝土孔隙液中的点蚀抑制

通过电化学噪声(ECN)、电化学阻抗(EIS)及极化曲线研究了四乙烯五胺(TEPA)在碳化模拟混凝土孔隙液中对Q345B碳钢点蚀的抑制机理. 结果表明: 通过吸附成膜和隔离Cl^-离子对钝化膜的侵蚀, TEPA浓度升高导致碳钢点蚀电位正移. 低浓度的TEPA会造成亚稳态蚀点形核速率略微增加, 但会降低其寿命. 随着TEPA浓度增加, 噪声电阻上升, 而亚稳态蚀点寿命和平均点蚀电量则迅速下降, 表明其明显加速了亚稳态蚀点修复, 当TEPA浓度达0.10 mol·L^-1时, 噪声峰消失, 基底电流趋于零, 蚀点全面钝化. TEPA不仅能抑制Cl^-离子引起的亚稳态和稳态蚀点生长, 还可吸附于钝化膜表面, 抑制膜的均匀溶解. 形貌观察表明, 亚稳态蚀点主要在稳态蚀点周围形核和生长, 并不断为稳态蚀点所吞并, 造成碳钢表面蚀坑一般沿平面而不是垂直方式扩展.     

Pitting corrosion in steel and electrochemical noise intensity

Electrochemical noise data in the presence of pitting corrosion were analyzed. A correlation between the intensity of the observed noise and mass loss of steel electrodes was recognized. The registered noise was decomposed into a set of band limited components using wavelet transform. It has been observed that the standard deviation of the chosen component was more strictly correlated with mass loss of electrodes than the standard deviation of the other components. The frequency band of the chosen component was adequate to the band where energy of transients, typical for pitting corrosion dominated. The measurement results were obtained only for the limited number of electrodes due to a very long time of noise observation.     

Theory of the wavelet analysis for electrochemical noise by use of the Laguerre functions

It is shown that the wavelet transform that uses the Laguerre function as a basis function is a useful tool to analyse the stationary electrochemical noise. Knowledge of the variance of the Laguerre wavelet of noise allows the Laplace transform of the correlation function to be found. The Laplace transform of the correlation function may be referred to the spectral density in the Laplace domain as well as to the operational spectral density of noise. It is shown that the operational spectral density of noise can be found not only by averaging over the ensemble of realizations of the noise process but also by averaging over the ensemble of Laguerre wavelets. The results obtained can be useful not only for analysis of electrochemical noise but also for analysis of any stationary random process, in particular for the time series analysis in econometric research.     

Theory for a wavelet analysis of electrochemical noise in the laplace space with use of two operational frequencies

The image of a random process in the Laplace space may be viewed as resulting from use of a oneway continuous wavelet transform with an exponential as the basic function, i.e. as resulting from the application of the Laplace wavelet. If the Laplace-wavelet variance of an electrochemical noise allows one to determine the Laplace transform of a time-correlation function, i.e. a factual operational spectral density of the noise, then the covariance of two Laplace waveletes of an electrochemical noise, each of which corresponds to its own operational frequency, allows one to verify a local consistency of the initial experimental noise data. The Laplace waveletes are applied rather widely. In fact, any stationary random process and stationary random time sequence can be described with operational noise spectra. Dedicated to the ninetieth anniversary of Ya.M. Kolotyrkin’s birth.     

Detection of SCC of 304 NG stainless steel in an acidic NaCl solution using electrochemical noise based on chaos and wavelet analysis

The stress corrosion crack (SCC) of 304 nuclear grade (NG) stainless steel (SS) in 0.5 mol/L NaCl+1.5 mol/L H₂SO₄ was monitored using electrochemical noise (EN) based on chaos theory, statistics and wavelet analysis. The results indicated that the SCC process was divided into three stages according to the transient features in the EN. In the beginning, compared with the sample without applied stress, the enhanced fluctuation amplitude in the electrochemical current noise (ECN) of the stressed samples was attributed to stress-enhanced pitting corrosion and uniform corrosion; then the fluctuations of ECN for all the samples decreased due to a coverage by the corrosion products; however, the ECN fluctuations of stressed sample were larger than the unstressed sample, suggesting that the stress enhanced the SCC initiation and propagation. Chaos analysis revealed that the correlation dimensions increase from 2.1 to 2.5 during the corrosion process, and the applied stress seems increase the complexity and uncertainty of the ECN signal.     

Wavelet transform-based Fourier deconvolution for resolving oscillographic signals

Fourier self-deconvolution is an effective means of resolving overlapped bands, but this method requires a mathematical model to yield deconvolution and it is quite sensitive to noises in unresolved bands. Wavelet transform is a technique for noise reduction and deterministic feature capturing because its time-frequency localization or scale is not the same in the entire time-frequency domain. In this work, wavelet transform-based Fourier deconvolution was proposed, in which a discrete approximation (such as A(2)) obtained from performing wavelet transform on the original data was substituted for the original data to be deconvolved and another discrete appropriate approximation (such as A(5)) was used as a lineshape function to yield deconvolution. Again, instead of the apodization function, the B-spline wavelet was used to smooth the deconvolved data to enhance the signal-to-noise ratio. As a consequence, this method does not suffer as badly as Fourier self-deconvolution from noises in the original data. Thus, resolution enhancement can be increased significantly, especially for signals with higher noise level. Furthermore, this method does not require a mathematical model to yield deconvolution; it is very convenient to deconvolve electrochemical signals.     

Wavelet transform-based analysis for electrochemical noise

Wavelet transforms are presented as a useful tool to analyse electrochemical noise data. Various concepts developed in the framework of wavelet transforms have been adapted to study electrochemical noise measurements. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about corrosion events occurring at a determined time-scale. Thus, this mathematical approach could become an alternative tool which solves the limitations of other more established procedures for the analysis of electrochemical noise data, such as statistical or Fourier transform-based methods.