Meng Jintao, Zhang Hao, Wang Xue, Zhao Yue
Science and Technology on Communication Security Laboratory, Chengdu 610041, China.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel). 2021 Nov 17;14(22):6954. doi: 10.3390/ma14226954.
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.
采用基于电阻传感器的大气腐蚀监测仪，通过实时记录动态腐蚀数据来研究户外大气环境中碳钢的腐蚀情况。对收集到的数据进行数据挖掘有助于揭示大气腐蚀的潜在机制。在本研究中，发现大多数统计相关系数并不适用于户外耦合腐蚀数据。为了处理在线耦合数据，从信息融合的角度提出了一种新的机器学习模型。其目的是量化不同环境因素在不同暴露时期对大气腐蚀的贡献。与腐蚀研究领域常用的人工神经网络和支持向量机等机器学习模型相比，实验结果证明了所提模型在测量大气因素重要性和腐蚀预测准确性方面对在线腐蚀数据的有效性和优越性。
