Authors:
Lorraine Marques Alves
;
Romulo Almeida Cotta
and
Patrick Marques Ciarelli
Affiliation:
Federal University of Espírito Santo, Brazil
Keyword(s):
Corrosion, Electrochemical Noise, Machine Learning, Wavelet Transform.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Learning in Process Automation
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
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. 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 paper, we analyze features extracted from electrochemical noise to identify types of corrosion, and we use machine learning techniques to perform this task. Experimental results show that the features obtained using wavelet transform are effective to solve this problem, and all the five evaluated classifiers achieved an average accuracy above 90%.