Print Email Facebook Twitter Corrosion classification through deep learning of electrochemical noise time-frequency transient information Title Corrosion classification through deep learning of electrochemical noise time-frequency transient information Author Homborg, A.M. (TU Delft Team Arjan Mol; Netherlands Defence Academy) Mol, J.M.C. (TU Delft Team Arjan Mol) Tinga, Tiedo (Netherlands Defence Academy) Date 2024 Abstract 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. Subject Machine learningElectrochemical noise transientsContinuous wavelet transformModulus maximaTime-frequency imagesCorrosion classification To reference this document use: http://resolver.tudelft.nl/uuid:ac2616a6-2636-4834-b0e6-9e859a57795e DOI https://doi.org/10.1016/j.engappai.2024.108044 ISSN 0952-1976 Source Engineering Applications of Artificial Intelligence, 133 Part of collection Institutional Repository Document type journal article Rights © 2024 A.M. Homborg, J.M.C. Mol, Tiedo Tinga Files PDF 1-s2.0-S0952197624002021-main.pdf 4.2 MB Close viewer /islandora/object/uuid:ac2616a6-2636-4834-b0e6-9e859a57795e/datastream/OBJ/view