基于核独立成分分析和支持向量数据描述的非线性系统故障检测方法  

Fault Detection Method for Non-linear Systems Based on Kernel Independent Component Analysis and Support Vector Data Description

作　　者：杨泽宇[1] 王培良[1,2] 

机构地区：[1]杭州电子科技大学新型电子器件与应用研究所,浙江杭州310018 [2]湖州师范学院信息与控制技术研究所,浙江湖州313000

出　　处：《信息与控制》2017年第2期153-158,共6页Information and Control

基　　金：国家自然科学基金资助项目(61573137)

摘　　要：复杂工业过程数据通常具有非高斯性和强非线性特征,为此提出了一种基于核独立成分分析和支持向量数据描述(KICA-SVDD)的非高斯非线性系统的故障检测方法.该方法首先运用核独立成分分析方法对数据进行特征提取,然后通过引入支持向量数据描述对独立主元成分进行建模,并计算相应的统计量及控制限,实现非高斯非线性系统下的故障检测.最后在Tennessee-Eastman(TE)过程上进行了仿真实验,结果表明所提出的方法降低了故障错分比例和漏检比例,验证了其可行性和有效性.Complex industrial process data have the features of non-Gaussian and strong non-linear,so we propose a fault detection method for non-Gaussian and non-linear systems based on the kernel independent component analysis and the support vector data description( KICA-SVDD). Firstly,we apply the KICA method to extract the features of the data. Then we use the SVDD to model the extracted leading independent component and to calculate the statistics and the control limits. So that the faults on the non-Gaussian and non-linear system can be detected. Finally,the experimental results on the Tennessee-Eastman( TE) process' s simulation study show that the proposed method reduces the fault misclassification ratio and the miss detection ratio,which verifies the proposed method's feasibility and validity.

关 键 词：核独立成分分析支持向量数据描述非高斯非线性故障检测 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]