Identification of Corrosive Substances through Electrochemical Noise using Wavelet and Recurrence Quantification Analysis

There are many types of corrosive substances that are used in industrial processes or that are the result of chemical reactions and, over time or due to process failures, these substances can damage, through corrosion, machines, structures and a lot of equipment. As consequence, this can cause financial losses and accidents. Such consequences can be reduced considerably with the use of methods of identification of corrosive substances, which can provide useful information to maintenance planning and accident prevention. In this paper, we analyze two methods using electrochemical noise signal to identify corrosive substances that is acting on the metal surface and causing corrosion. The first method is based on Wavelet Transform, and the second one is based on Recurrence Quantification Analysis. Both methods were applied on a data set with six types of substances, and experimental results shown that both methods achieved, for some classification techniques, an average accuracy above 90%. The obtained results indicate the both methods are promising.