Journal
CORROSION SCIENCE
Volume 198, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.corsci.2022.110119
Keywords
Electrochemical Impedance Spectroscopy (EIS); Corrosion; Machine Learning
Funding
- Engineering and Physical Sciences Research Council (EPSRC) [EP/S004963/1]
- EPSRC [EP/T517823/1]
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This study explores the potential use of machine learning algorithms to interpret electrochemical impedance spectroscopy (EIS) data, focusing on classification and fitting scenarios. The traditional approach based on equivalent circuit modeling is difficult to automate and has limited industrial applications.
Electrochemical impedance spectroscopy (EIS) interpretation is generally based on modelling the response of a corroding system by an equivalent circuit. Although effective, the approach is difficult to automate and uptake in an industrial context is limited. Machine Learning (ML) algorithms can solve complex tasks after a training process and this work explores the possibility of using ML to interpret EIS data. Two scenarios are considered: classification, i.e. identifying which equivalent circuit is associated to an EIS spectrum, and fitting, i.e. estimating the numeric values of the components of an equivalent circuit.
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