改进的基于数据重构的KPCA故障识别方法

Improved KPCA Fault Identification Method Based on Data Reconstruction

核主元分析(KPCA)方法相对于主元分析(PCA)方法在非线性过程监测方面具有一定的优势,但是KPCA很难找到由特征空间到原始空间的逆映射函数,这给基于KPCA的故障诊断带来了很大的障碍.为此,在KPCA故障数据重构方法的基础上,对故障识别指标进行改进.改进后的方法既能够识别单变量引起的故障,又能识别多变量引起的故障,而且减少了指标计算过程中的运算量,避免了传统故障识别方法只能实现单变量故障追溯的缺陷.将提出的故障识别方法在田纳西过程中进行了仿真研究,结果表明所提方法的有效性.

Compared with the principal component analysis（PCA） method,kernel principal component analysis（KPCA） method has more advantages in the monitoring of nonlinear processes.However,it is difficult to find an inverse mapping function from the feature space to the original space for KPCA,resulting in great difficulties for the KPCA-based fault diagnosis.To solve this problem,the fault identification index was improved on the basis of KPCA fault data reconstruction method.The improved method could identify both univariate faults and multivariate faults.In addition,the proposed method could also reduce calculation and avoid the defect that the traditional fault detection methods could only identify univariate faults.The simulation results indicated the feasibility and effectiveness of the proposed method by testing it in the Tennessee-Eastman process.