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Authors: Lorraine Marques Alves ; Romulo Almeida Cotta and Patrick Marques Ciarelli

Affiliation: Federal University of Espírito Santo, Brazil

ISBN: 978-989-758-222-6

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%.

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Paper citation in several formats:
Marques Alves, L.; Almeida Cotta, R. and Marques Ciarelli, P. (2017). Identification of Types of Corrosion through Electrochemical Noise using Machine Learning Techniques.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 332-340. DOI: 10.5220/0006122403320340

@conference{icpram17,
author={Lorraine Marques Alves. and Romulo Almeida Cotta. and Patrick Marques Ciarelli.},
title={Identification of Types of Corrosion through Electrochemical Noise using Machine Learning Techniques},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={332-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006122403320340},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Identification of Types of Corrosion through Electrochemical Noise using Machine Learning Techniques
SN - 978-989-758-222-6
AU - Marques Alves, L.
AU - Almeida Cotta, R.
AU - Marques Ciarelli, P.
PY - 2017
SP - 332
EP - 340
DO - 10.5220/0006122403320340

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