Cluster and discriminant analysis of electrochemical noise data

In a typical pitting system of Q235 low carbon steel in 0.50 mol/L NaHCO3 + NaCl solutions, a new method is presented to analyze electrochemical noise (EN) data, including potential (E) and current (I) noise, and identify its corresponding pitting states. The proposed method is based on cluster analysis (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 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. The validity of the cluster/discriminant analysis has been proved in the studied pitting system.