用于故障检测的集成核主分量分析

Ensemble kernel principal component analysis for fault detection

针对复杂环境下的多变量工业过程在线故障检测问题,提出基于集成核主分量分析的解决方法.该方法首先求出样本映射后的无限维空间的多组近似基,将主分量分析问题特征向量的解空间限定在近似基张成空间求解;然后集成特征向量和特征值,并计算Hotelling 2统计量和平方预报误差;最后据此判断检测结果.该方法对Tennessee Eastman过程故障检测样本进行测试,并与其他两种方法进行对比.测试结果表明了所提出方法的有效性.

An ensemble principal component analysis is presented for online multivariable process fault detection on complicated conditions. In reproducing kernel Hilbert space（RKHS） spanned by the mapped samples, groups of basis（approximate） are found. Eigenvectors for principal component analysis problem are limited to the spaces spanned by approximate basis. The eigenvectors and eigenvalues in different subspace are integrated to make up for the approximation. Hotelling T2 and squared prediction error are calculated according to integrated eigenvectors and eigenvalues. Experiments on Tennessee Eastman is presented to demonstrate the effectiveness of the ensemble learning.