基于DMVU-OCSVM的故障诊断方法  

Fault diagnosis method based on dynamic maximum variance unfolding and one-class support vector machine

作　　者：邓晓刚[1] 田学民[1] 

机构地区：[1]中国石油大学(华东)信息与控制工程学院,山东东营257061

出　　处：《化工学报》2011年第8期2146-2151,共6页CIESC Journal

基　　金：山东省自然科学基金项目(Y2007G49);中央高校基本科研业务费专项资金(10CX04046A)~~

摘　　要：针对工业过程的非线性和动态特性,提出一种基于动态最大方差展开(DMVU)和单类支持向量机(OCSVM)的故障诊断方法DMVU-OCSVM。为了分析数据的动态特性和非线性,应用流形学习技术DMVU提取数据变量中的非线性动态流形特征。基于所提取的流形特征信息建立OCSVM统计模型,并构造非线性监控统计量实时检测过程故障。在连续搅拌反应器(CSTR)系统上的仿真结果说明,本文提出的方法能够比OCSVM更有效地检测过程故障。In order to analyze nonlinear and dynamic characteristics of industrial process,a new method combining dynamic maximum variance unfolding and one-class support vector machine（DMVU-OCSVM）was presented.Manifold learning method DMVU was applied to obtain nonlinear and dynamic manifold features.OCSVM was used to build statistic model based on manifold information and nonlinear monitoring statistic was constructed to detect fault online.The simulation results on continuous stirred tank reactor system showed that the proposed method could detect process fault more effectively.

关 键 词：故障诊断 动态最大方差展开 单类支持向量机 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]