Resumo:
Corrosion is a global problem, which implies costs in industrialized countries of up to
4.5% of GDP, with either economic, but also social and environmental impacts. In the
case of Brazil, the waste of water supply networks due to leaks loss is quite significant
and much of it is caused by network degradation, indicating that corrosion control
should be promoted whenever possible. This study proposes a corrosion monitoring
system, in system subject to the use of inhibitor, with the approach of passive
technique for monitoring corrosion by electrochemical noise (EN), in which the
classification of events in a corrosion sensor by EN is part of methodological study for
structural integrity (or “health”) monitoring system (SHM). Due to very dynamic and
stochastic nature of the signal, this study and analysis of EN measurements (ENM)
considers numerical and graphic characteristics of two corrosion systems both in saline
aqueous solution: carbon steel and stainless steel. These experiments are repeated for
accumulating data, which allow the generation of several graphs in time and frequency
domains, from which at least one characteristic is extracted, which has a good
correlation with data from corrosion processes. Then, based on a supervised machine
learning system, the training data allows the model to be calibrated. From the test
data, the correctness rate of the model above 50% is verified.
