为解决有限样本数据下工艺管道中腐蚀速率难以估算的问题，提出一种基于核主成分分析（KPCA）和支持向量机（SVM）的腐蚀速率预测的方法。采用核主成分分析方法有效融合多种腐蚀影响因素，剔除核主成分分析结果中贡献率低的主元变量，将贡献率大的主元变量作为输入，腐蚀速率作为输出，建立了支持向量机模型，并预测了管道腐蚀速率。采用工艺管道实际工程数据检验KPCA-SVM模型预测腐蚀速率的效果，结果表明，基于核主成分分析和支持向量机方法的管道腐蚀速率预测误差较小，能够获得准确的预测结果。

In order to estimate the corrosion rate of process pipeline under limited condition data, a method based on kernel principal component analysis (KPCA) and support vector machine (SVM) was proposed. The kernel principal component analysis method was used to effectively integrate the factors that affect the corrosion rate of pipeline. The kernel components with low contribution rate were discarded. The principal components with higher contribution rate were taken as input variables of the support vector machine, and the corrosion rate was taken as output. The support vector machine model was established to predict the corrosion rate of pipelines. The prediction model was verified by engineering data of process pipelines. The results show that prediction error of the method based on kernel principal component analysis and support vector machine was low, and accurate prediction data could be obtained.