A Random Forest–Based Intelligent Framework for Automated Electrochemical Impedance Analysis in Industrial Corrosion Monitoring

This project aims to develop an intelligent framework based on the Random Forest algorithm for the automated interpretation of Electrochemical Impedance Spectroscopy (EIS) data to monitor corrosion processes in industrial applications. In the case of standard Interpretations of EIS data, expert knowledge is needed as the data can be very extensive, and the data can also be very extensive, making it hard to apply in real-time in dynamic situations and environments in a continuous or real-time manner. The intelligent framework seeks to relieve these limitations by providing automated, efficient, and accurate characterizations of corrosion processes. This includes the acquisition of EIS data, the subsequent processes of feature extraction from the

impedance spectra, and the classification and/or predictive training of a Random Forest model regarding states and mechanisms of corrosion. This intelligent framework seeks to provide quick and dependable information regarding the degradation of materials. This in turn helps to increase the effectiveness of predictive maintenance practices while also reducing the possibility of a critical failure by corrosion. The framework's capacity to autonomously process complicated EIS comprehension data sets offers substantial benefits for continuous and causal monitoring and intervention in various industrial applications.

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