信息与控制2017，Vol.46Issue(2)：153-158,6.DOI:10.13976/j.cnki.xk.2017.0153
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
摘要
Abstract
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Key words
kernel independent component analysis/support vector data description/non-Gaussian/non-linear/fault detection分类
信息技术与安全科学引用本文复制引用
杨泽宇,王培良..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[J].信息与控制,2017,46(2):153-158,6.基金项目
国家自然科学基金资助项目(61573137) （61573137）
