Corrosion classification through deep learning of electrochemical noise time-frequency transient information

This paper for the first time treats the interpretation of electrochemical noise time-frequency spectra as an image
classification problem. It investigates the application of a convolutional neural network (CNN) for deep learning
image classification of electrochemical noise time-frequency transient information. Representative slices of these
spectra were selected by our transient analysis technique and served as input images for the CNN. Corrosion data
from two types of pitting corrosion processes serve as test cases: AISI304 and AA2024-T3 immersed in a 0.01M
HCl and 0.1M NaCl solution between 0 and 1ks after immersion, respectively. Continuous wavelet transform
(CWT) spectra and modulus maxima (MM) are used to train the CNN, either individually or in a combined form.
The classification accuracy of the CNN trained with the combined dataset is 0.97 and with the two individual
datasets 0.72 (only CWT spectrum) and 0.84 (only MM). The ability to additionally classify a more progressed
form of pitting corrosion of AA2024-T3 between 9 and 10ks after immersion indicates that the proposed method
is sufficiently robust using combined datasets with CWT spectra and MM. The pitting processes can effectively be
detected and classified by the proposed method. The most important contribution of the present work is to
introduce a novel procedure that decreases the classical need for large amounts of raw data for training and
validation purposes, while still achieving a satisfactory classification robustness. A relatively small number of
individual signals thereby generates a multitude of input images that still contain all relevant kinetic information
about the underlying chemo-physical process