# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## 3 Results
### Corrosion Type Identification  
The typical methods for distinguishing corrosion types includes AE parameters cluster (i.e., k-means) and the ensemble learning method (i.e., random forest). These methods only utilize some parameters of the AE waveform (raw or pre-treated) or rely on big data. To identify the different corrosion types in the corrosion process fast and effectively, a 2D pattern recognition algorithm was established by Matlab. This processing algorithm provides a new feasible method for the identification of different acoustic emission data directly from the waveform of the AE signal without the extraction of AE parameters.  
In 2D pattern recognition analysis, the luminance information of the object surface is related to the illumination and reflection coefficient. Otherwise, the structure and illumination of the objects in the scene are independent. The reflection coefficient and the related objects are the same. We can explore the structure information  
Other corrosion types were monitored by AE, aiming to calculate the similarities among them. The pitting corrosion of stainless steel in the presence of different concentrations of chloride ions and the uniform corrosion of carbon steel in sulphuric acid solution was monitored by the same device as Figure 1. The specimens of the crevice corrosion of stainless steel were pretreated based on ASTM G39-1999(2011) and monitored by the method of [PERSON] [18], and the AE waveforms of different types of corrosion were arranged in a time sequence. Figure 11 shows the continuous AE waveform graphics of the different corrosion experiments. These continuous AE waveforms were obtained by arranging the waveform of each AE event one by one in the time sequence. These waveform graphics were imported into Matlab at first, then the 2D pattern recognition algorithm was performed as described by formula (1) to (5) to obtain the similarities of the different graphics. The calculation results are exhibited in Table 3.  
Figure 11: _Cont._  
According to the calculation results, there was a high similarity between the pitting corrosion of carbon steel and that of stainless steel. The occluded cell of the pitting corrosion on carbon steel was  
Figure 11: Different continuous AE graphics: (**a**) pitting corrosion of carbon steel, (**b**) pitting corrosion of stainless steel, (**c**) crevice corrosion of stainless steel, (**d**) uniform corrosion of carbon steel, (**e**) noise.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Pitting Corrosion** & **Crevice Corrosion** & **Uniform** & **Conrosion Carbon** & **Noise** \\  & **Carbon Steel** & **Stainless Steel** & **Stainless Steel** & **Stainless Steel** & **Stainless Steel** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The similarity of the different types of corrosion.  
not stable in the performed experiments. The repeating process of the occluded cell formation and rupture caused the open pits on the surface of the carbon steel. In contrast, the pitting corrosion of stainless steel had a relatively strong occlusion effect, such that the corrosion pit of the stainless steel often had a small mouth with relatively larger depth. However, these two kinds of pitting corrosion were all caused by the occlusion effect, from which they had a similar acoustic emission process (83.23% similarity) as shown in Table 3. In addition, the corrosion process of the 2D images of the pitting corrosion and crevice corrosion also had a high degree of similarity (73.03%), which is related to their corrosion mechanisms being relatively similar [34]. On the other hand, the graphics of the uniform corrosion had a low similarity with the other types of corrosion, indicating that this data processing approach can identify localized corrosion from uniform corrosion. In addition, the AE graphic of the artificial noise from the stirring of the solution also had low similarities with all the corrosion graphics.