Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Abstract

An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.

Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution

“…Considering the universality of Gaussian distribution in the natural world [57], the normal distribution was first attempted, and results are reported in the form of normality tests. Existing modelling attempts fail to predict pitting corrosion because of the uncertainty related to the process.…”

Physics-Informed, Data-Driven Model for Atmospheric Corrosion of Carbon Steel Using Bayesian Network

“…Equation ( 10) is the formula for the corrosion rate obtained through the calibrated model. However, this equation has units of g/m 2 y; therefore, when this equation is converted to the same units as those in Equation ( 9), it is equivalent to Equation (11). The noticeable differences observed in the values of each parameter when comparing Equation ( 9) and the calibrated model (Equation (11)) can be attributed to two reasons.…”

“…However, this equation has units of g/m 2 y; therefore, when this equation is converted to the same units as those in Equation ( 9), it is equivalent to Equation (11). The noticeable differences observed in the values of each parameter when comparing Equation ( 9) and the calibrated model (Equation (11)) can be attributed to two reasons. First, Equation ( 9) takes into account the additional effects of SO 2 and temperature.…”

“…To prevent corrosion, an appropriate corrosion deterioration model is necessary. However, because of the limited availability of data for the parameter estimation and the high level of uncertainty in predicting results, a practical application of the corrosion deterioration model is difficult [9][10][11]. Previous studies have modeled multiple physical phenomena related to atmospheric corrosion using commercial software such as COMSOL Multiphysics (4.2 and 5.6) [12][13][14][15][16][17].…”

Developing a regional environmental corrosion model for Q235 carbon steel using a data-driven construction method

“…All the environmental factors listed in Table 1 can affect metal corrosion (Oesch, 1996;Chen et al, 2005;Corvo et al, 2005;Syed, 2008;Rouillard et al, 2009;Li et al, 2013;Nguyen et al, 2013;Nyrkova et al, 2013;Wang et al, 2013;Pei et al, 2020), and coupling relationships exist among these factors (Meng et al, 2021). The role of each environmental factor in the corrosion of carbon steel depends on the region.…”