Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov–Sinai entropy. (15th May 2017)

• Record Type: Journal Article
• Authors: Xia, Da-Hai; Ma, Chao; Song, Shizhe; Jin, Weixian; Behnamian, Yashar; Fan, Hongqiang; Wang, Jihui; Gao, Zhiming; Hu, Wenbin
• Is Part Of: Corrosion science. Volume 120(2017)
• Page Start: 251
• Page End: 256
• Publication Date: 2017-05-15
• Subjects: A. carbon steel -- B. weight loss -- C. atmospheric corrosion -- Kolmogorov–Sinai entropy -- Image processing; Corrosion and anti-corrosives -- Periodicals; 620.11223
• DOI: 10.1016/j.corsci.2017.02.015
• Languages: English
• ISSNs: 0010-938X

Abstract

Graphical abstract: Highlights: Atmospheric corrosion is assessed by fuzzy Kolmogorov–Sinai (K–S) entropy. Fuzzy K–S entropy for horizontal and vertical orientations are calculated. Fuzzy K–S entropy decreases as corrosion degree is more and more serious. There is a linear relationship between fuzzy K–S entropy and weight loss. Abstract: Fuzzy Kolmogorov–Sinai (K–S) entropy was used to characterize the irregularity of the spatial information distribution in a corrosion image. The fuzzy K–S entropy was measured for horizontal and vertical orientations of corrosion images of steel samples exposed to a marine atmosphere over 191 days. The fuzzy K–S entropy for horizontal and vertical orientations decreased as the corrosion propagated. A relationship between the fuzzy K–S entropy and the weight loss in the steel sample can be presented with a linear mathematical expression. Fuzzy K–S entropy was found to be a semiquantitative and fast method to quantify atmospheric corrosion.