Research On The Methods Of Analyzing Electrochemical Noise And Its Application In Soil Corrosion Processes

In this paper,researching the methods of analyzing electrochemical noise(EN) was considered as the main line.The pretreatment for EN raw data before other time/frequency domain analysis were compared firstly,and then the influence of DC drifts on the analysis results was researched.After analyzing the distribution characteristics of the potential and current noise(E and log|I|) from a typical pitting corrosion system,pattern recognition(PR) was proposed to study the relationship between data points(E,log|I|) and different pitting states.In order to reduce the sample number for the PR,some statistical parameters were used as variables in place of E and log|I|,and its validity was also verified in the same system.For ulcerous corrosion which has not characteristic EN time records,the PR with some EN statistical parameters as variables was applied to study the relationship between EN and its corresponding different corrosion states.Based on the above analysis,EN measurement was proposed to research the characteristics of the soil corrosion to establish its recognition technology.It focused on the EN analysis methods in soil corrosion research. Finally,the characteristics and mechanism of the complex soil corrosion were studied by EN,electrochemical impedance spectroscopy(EIS),polarization curve,loss-weight method and corrosion morphology observation,and good agreement was obtained.The main research results were shown as following:Polynomial fitting(PF) and wavelet transform(WT) were chosen to remove trends of EN from typical pitting system.The effects of trends on statistical parameters,power spectral density(PSD),absolute energy distribution plot(AEDP) and relative energy distribution Plot(EDP) were discussed.Furthermore,the indication of selecting the appropriate method was put forward.The results showed that the common statistical parameters except mean value would not change distinctly.Based on PSD and WEDPs,it can be found that the higher order polynomial and WT would reduce the low frequency signals more heavily,so lower order(m=1～3) polynomial was recommended.The appearance of plateau in low-frequency part of PSD plots or EDP could be regarded as the indication of the best selection of m.For large numbers of EN data,PF using window technique has been applied to remove trend.The order and window size influenced corporately the removing results and the EDP characteristics.In order to attenuate the low-frequency components without damage the useful information,the lower polynomial order(no bigger than 3) and appropriate size(between 1024 and 4096) which was determined by characteristics of EN fluctuations should be selected.In a typical pitting system of Q235 low carbon steel in 0.50mol/L NaHCO₃+NaCl solutions,a new method was presented to analyze EN data and identify its corresponding pitting states.The proposed method is based on k-means cluster analysis(k-means CA) and discriminant analysis(DA).Firstly,E and log|I| were determined as the variables for clustering.Then,data points(E,log|I|) of the EN groups from different pitting states were classified by k-means CA to two clusters,which relate to the metastable state(Cluster 1) and stable state(Cluster 2) respectively.When a group of(E,log|I|) data points were dispersed stochastically into two clusters,it relates to the intermediate state that was defined to describe the transformation from the metastable pitting to stable pitting.Based on the obtained clustering results,a discriminant function(s) was established to discriminate the ungrouped EN data from the similar pitting processes and thus its corresponding pitting state could be determined by the cluster distribution result.In order to reduce data number of the cases reasonably without loss of useful information,principal component analysis(PCA),hierarchical agglomerative cluster analysis(HACA) and DA were applied for analyzing EN statistical parameters from the similar pitting system as described as before.Firstly,according to the PCA results,the EN mean value((?) and(?) ) as well as standard deviation(σ_E andσ_I) were determined as the descriptors for clustering.Then,using the selected four statistical parameters as variables, the cases from different pitting states were classified by the HACA to three clusters,which relates to the metastable state,intermediate state and stable state,respectively.It shows a good agreement with the classification obtained from k-means CA with E and log|I| as variables.Based on the cluster results,the pitting states of the ungrouped data points from the similar pitting processes also can be distinguished according to the established discriminant function(s).The time and frequency domains of the EN from 0.5mol/L NaCl ulcerous corrosion would not change with the development of the corrosion time,so it is difficult to distinguish the different corrosion states by statistical parameters,PSD or EDP and then to determine the proper k value.Thus,the PCA/HACA was proposed to analyze this kind of EN signals.Based on the PCA,(?),σ_E,σ_I andσ_I/(?) were determined as the descriptors for clustering.And then,using them as variables,the cases from the ulcerous corrosion would be classified by the HACA to three clusters,which relates to the quick germination state,horizontal development state and vertical development state.Good agreement between classification and corrosion morphology was gained.According to the above analyses,EN technique was applied to study the corrosion behaviors of X70 steel in 30℃Xinjiang Ku'erle saline soil(moisture content 1.04%) and 35℃Ku'erle soil with low moisture(moisture content 1.04～3.12%) during the initial corrosion period(0～7d).The obtained EN data were analyzed by the PCA,HACA and WT. After the PCA applied,σ_E,(?) and(?) were considered as the descriptors to characterize EN distributions.Then,using them as variables,for the 30℃Ku'erle soil with 1.04% moisture,three different local corrosion states can be classified clearly by the HACA, including instable germination state,quick development state and stable development state. For the 35℃Ku'erle soil with low moisture,only two corrosion states were classified,i.e. quick development state and stable development state.Furthermore,the extent of the local corrosion in different moisture soil also can be differentiated.According to the current EDP, EIS and corrosion morphology,the characteristics of the different corrosion stages were further proved.The results showed that the PCA/HACA method should be successful for the analysis of the EN signals from the soil corrosion system.