基于核主元分析的非线性动态故障诊断

核主元分析是一种非线性主元分析方法，充分利用核函数来解决非线性映射问题，在高维特征空间中确定主元，具有很好的非线性逼近能力。同时，利用非线性最小二乘法实现核主元分析的变量重构，来识别故障源。将核主元分析应用于连续搅拌釜式反应器系统（CSTR）的故障诊断过程中，仿真结果表明该方法对于故障的检测和故障源的识别都优于线性主元分析法的诊断效果。

Fault Diagnosis for Nonlinear Dynamic System Using Kernel Principal Component Analysis

Kernel principal component analysis is a type of nonlinear principal component analysis,which uses kernel function to deal with nonlinear problem,and computes principal components in a high dimensional feature space.A method of fault diagnosis for nonlinear dynamic system by kernel principal component analysis was presented,and the root of fault causes is identified by the reconstructed variables with nonlinear least squares optimization.The simulations in the continuous stirred-tank reactor (CSTR) indicate that the performances of process monitoring and fault diagnosis by the presented method are superior to that by principal component analysis.