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Identification of Corrosive Substances and Types of Corrosion Through Electrochemical Noise Using Signal Processing and Machine Learning

  • Lorraine Marques Alves
  • Romulo Almeida Cotta
  • Patrick Marques CiarelliEmail author
  • Evandro O. T. Salles
  • Klaus F. Côco
  • Jorge L. A. Samatelo
Article
  • 117 Downloads

Abstract

Several systems in industries are subject to the effects of corrosion, such as machines, structures, and a lot of equipment. As consequence, the corrosion can damage structures and equipment, causing financial losses and accidents. Among the most common types is the localized corrosion, and it is present in most industrial processes and is the most difficult to detect. Such consequences can be reduced considerably with the use of methods of detection, analysis and monitoring of corrosion in hazardous areas, which can provide useful information to maintenance planning and accident prevention. In this work, we analyze some features extracted from electrochemical noise for the classification of different types of localized corrosion. Furthermore, we use some techniques to identify corrosive substances that may cause corrosion in materials. For both tasks, we apply signal processing and machine learning techniques. Experimental results show that the features obtained using wavelet transform and recurrence quantification analysis are effective to solve both tasks: the corrosion identification and the classification of substances. Almost all evaluated machine learning techniques achieved an average accuracy above 90%.

Keywords

Corrosion Electrochemical noise Machine learning Wavelet transforms Recurrence quantification analysis 

Notes

Acknowledgements

Evandro O. T. Salles would like to thank FAPES - Fundação de Amparo à Pesquisa e Inovação do Espírito Santo by the partial support under grant 244/2016. Patrick Marques Ciarelli thanks the partial funding of his research work provided by CNPq (Grant 312032/ 2015-3).

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Copyright information

© Brazilian Society for Automatics--SBA 2018

Authors and Affiliations

  1. 1.Federal University of Espírito SantoVitória-ESBrazil

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