摘要
针对碳钢在大气腐蚀过程中影响变量多且作用机制复杂的问题,提出一种基于综合智能模型的重要变量挖掘框架,利用该框架可以挖掘影响碳钢早期大气腐蚀的重要环境变量及其对腐蚀电偶电流产生的影响.本文通过大气腐蚀监测仪(ACM)收集了我国5个试验站点的大气腐蚀数据,首先,构建了随机森林(RF)、梯度提升回归树(GBRT)和BP神经网络(BPNN)三种机器学习模型;其次,利用多模型集成重要变量选择算法(MEIVS)量化环境变量的重要性并提取影响碳钢早期大气腐蚀的重要环境变量;最后,绘制了环境变量与腐蚀电偶电流的局部依赖曲线(PDP).仿真结果显示,MEIVS算法挖掘出的重要环境变量更符合大气腐蚀的先验规律;PDP与MEIVS算法的结论具有很好的一致性,重要环境变量对应的PDP的变化幅度大,且PDP的变化趋势能够反映环境变量对腐蚀电偶电流的影响.
Machine learning algorithms are widely used to predict the corrosion rate of materials in a specific environment.However,the interpretability of such black-box models is poor,which hinders their application in the field of material corrosion.Therefore,to increase algorithm transparency in practical applications,the causal relationship in the material corrosion phenomenon based on machine learning models needs to be further explored.To solve the aforementioned problems,this study analyzed the corrosion process of carbon steel in the atmosphere with many variables and complex mechanisms and proposed an important variable mining framework based on the comprehensive intelligent model.This framework can mine the important environmental variables that affect the early atmospheric corrosion of carbon steel and their influence on the corrosion galvanic current.This study collected the hour-level atmospheric corrosion data,including relative humidity,temperature,rainfall,and O_(3),SO_(2),NO_(2),PM_(2.5),and PM_(10) concentrations,of 45#carbon steel from five test sites in China using the atmospheric corrosion monitor of the China Meteorological Administration.To ensure the stability of the results,three machine learning models with different fitting strategies,namely,random forest,gradient boosted regression trees,and backpropagation neural network,are constructed.Then,the multimodel ensemble important variable selection(MEIVS)algorithm is used to quantify the importance of environmental variables and extract important environmental variables that severely affect the early atmospheric corrosion of carbon steel.Eventually,the partial dependence plot(PDP)of the environmental variables and corrosion galvanic current is drawn.Based on the simulation results,three significant conclusions are obtained:(1)Compared with Pearson’s and Spearman’s correlation coefficients,the important environmental variables mined using the MEIVS algorithm are more consistent with the prior law of early atmospheric corrosion of carbon steel.Relative humidity,temperature,and rainfall have the most significant impact on the early atmospheric corrosion of carbon steel,and O3 has a considerable influence on the early atmospheric corrosion of carbon steel in Sanya.Moreover,other pollutants in various regions have a weak impact on the early atmospheric corrosion of carbon steel.(2)PDP shows that,in most cases,the corrosion galvanic current of 45#carbon steel is negatively correlated with temperature and positively correlated with relative humidity.(3)PDP and MEIVS are well consistent.The simulation reveals that PDP corresponding to important environmental variables has a greater range of change,and the changing trend of PDP can reflect the influence of environmental variables on the corrosion galvanic current.
作者
张明
付冬梅
张达威
马菱薇
邵立珍
ZHANG Ming;FU Dong-mei;ZHANG Da-wei;MA Ling-wei;SHAO Li-zhen(Shunde Graduate School,University of Science and Technology Beijing,Foshan 528300,China;School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Beijing Engineering Research Center of Industrial Spectrum Imaging,Beijing 100083,China;Institution for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China;National Materials Corrosion and Protection Data Center,Beijing 100083,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2023年第3期407-418,共12页
Chinese Journal of Engineering
基金
科技部科技基础资源调查专项资助项目(2019FY101404)
北京科技大学顺德研究生院科技创新专项资助项目(BK20AE004)。
关键词
大气腐蚀
碳钢
模型集成
重要变量提取
局部依赖曲线
atmospheric corrosion
carbon steel
model integration
important variable extraction
partial dependence plot
