基于机器学习分类算法解析EIS数据的有机涂层性能评价方法
An Organic Coating Performance Assessment Method Based on Machine Learning Classification Algorithms and EIS Data
纪皓迪 1马小兵1
作者信息
- 1. 北京航空航天大学 可靠性与系统工程学院,北京 100191
- 折叠
摘要
目的 基于机器学习分类算法快速评估有机涂层的防腐性能.方法 通过实验室加速试验模拟涂层真实的退化过程,并根据测得的电化学数据,分析不同退化阶段的等效电路元件参数.随后,采用随机抽样方法获取大量数据,用于机器学习模型训练.通过对比支持向量机(SVM)、k最近邻(k-NN)和随机森林(RF)3 种不同的机器学习算法,以及多种输入特征集训练的涂层性能分类器模型的准确率,分析最适合用于涂层性能快速评估的机器学习算法和电化学特征.结果 根据不同输入特征训练的k-NN和RF模型均表现出良好的预测效果,而SVM模型的预测效果相对较差.根据不同频率范围训练的分类器模型中,在低频区表现最佳,而在高频区表现较差.结论 基于阻抗虚部、虚部+实部和阻抗模值3种输入特征训练的RF分类器模型的预测效果最准确.不同频率区间内,低频区的阻抗特征更能准确表征涂层性能.
Abstract
The work aims to rapidly evaluate the corrosion resistance performance of organic coatings using machine learn-ing classification algorithms.Laboratory accelerated tests were conducted to simulate the actual degradation process of coatings.The equivalent circuit parameters at different degradation stages were analyzed based on measured electrochemical data.Sub-sequently,a large amount of data were obtained for machine learning through random sampling.By comparing Support Vector Machine(SVM),k-Nearest Neighbors(k-NN),and Random Forest(RF)algorithms,as well as the accuracy of coating perform-ance classifier models trained with various input feature sets,the most suitable machine learning algorithms and electrochemical features for rapid coating performance evaluation were analyzed.Classifier models trained with k-NN and RF models,demon-strated good predictive performance,while the SVM model showed relatively poorer predictive performance.Among the mod-els trained with different frequency ranges,those trained with low-frequency data performed the best,whereas those trained with high-frequency data performed relatively worse.The RF classifier model trained with impedance imaginary part,imaginary part&real part,and impedance modulus as input features demonstrates the most accurate predictive performance.Within different frequency ranges,impedance features from the low-frequency range are more effective in accurately characterizing coating per-formance.
关键词
有机涂层/分类算法/机器学习/电化学阻抗谱/支持向量机/k最近邻/随机森林Key words
organic coating/classification algorithm/machine learning/electrochemical impedance spectroscopy/Support Vector Machine/k-Nearest Neighbor/Random Forest引用本文复制引用
出版年
2024
NETL
NSTL
万方数据