Developing Real-time Corrosion Monitoring: A Cutting-Edge Fusion of Electrochemical Noise Data and Machine Learning Techniques

Abstract

The study addresses effectively monitoring and controlling the corrosion process using electrochemical noise analysis in different scenarios. It explores the challenges in feature extraction and analytical methods. It also proposes novel systematic approaches to overcome these challenges using deep learning models such as stochastic neighbour embedding (t-SNE) and principal component analysis (PCA). This work provides a potential quantification analysis method for online corrosion monitoring and control, widely considered the industry standard.