Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis

Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis

Blog Article

Abstract Electrochemical noise (EN) is a crucial technique in the monitoring of corrosion systems due to its ability to provide real-time, non-intrusive insights into the corrosion process.By measuring the spontaneous fluctuations in voltage and current that occur naturally in a corroding system, EN allows for sara stedy stand aid the detection of localised corrosion events, such as pitting, without the need for external perturbation.In this investigation, a multivariate statistical process monitoring framework (MSPC) based on the use of deep learning models and principal component analysis (PCA) is proposed.Electrochemical noise associated with uniform corrosion is segmented with a sliding window, with the segments converted to images g35 coupe fender from which features are extracted with deep learning models.Finally, these features are used to construct a principal component model that can be used to detect deviations from uniform corrosion.