基于核状态空间ICA的非线性动态过程故障检测方法

上海交通大学学报（自然版） ›› 2014, Vol. 48 ›› Issue (07): 971-976.

• 自动化技术、计算机技术 • 上一篇下一篇

基于核状态空间ICA的非线性动态过程故障检测方法

蔡连芳，田学民，张妮

（中国石油大学信息与控制工程学院，山东青岛 266580）

收稿日期:2013-06-08 出版日期:2014-07-28 发布日期:2014-07-28
基金资助:
国家自然科学基金项目（61273160），山东省自然科学基金项目（ZR2011FM014），山东省博士基金项目（BS2012ZZ011）

A Nonlinear Dynamic Process Fault Detection Method Based on
Kernel State Space Independent Component Analysis

CAI Lianfang,TIAN Xuemin,ZHANG Ni

（College of Information and Control Engineering， China University of Petroleum， Qingdao 266580， Shandong, China）

Received:2013-06-08 Online:2014-07-28 Published:2014-07-28

摘要/Abstract

摘要：

针对工业过程的非线性和动态特性，提出一种基于核状态空间独立元分析的故障检测方法.采用核规范变量分析法将非线性动态过程数据映射到核状态空间，得到去相关的状态数据.对状态数据的各时延协方差矩阵进行加权求和得到状态数据的时序结构矩阵，进而建立ICA统计模型，从状态数据中提取独立元特征数据，并构造监控统计量检测过程故障.在Tennessee Eastman 过程上的故障检测结果表明，相比于传统的基于动态核主元分析的故障检测方法，该方法更加灵敏地检测到故障的发生，提高故障检测率.

关键词: 故障检测, 非线性, 动态特性, 核规范变量分析, 独立元分析, 故障检测率

Abstract:

A fault detection method based on kernel state space independent component analysis (KSSICA) was proposed in this paper considering the nonlinear and dynamic characteristics of industrial processes. Kernel canonical variate analysis (KCVA) was adopted to project the nonlinear and dynamic process data into the kernel state space, and the state data which were uncorrelated were obtained. Based on the state data’s time structure matrix which is the weighted sum of the state data’s different timedelayed covariance matrices, an ICA statistical model was constructed to extract the independent component feature data from the state data, and the monitoring statistics were built to detect process faults. The fault detection results on the Tennessee Eastman benchmark process demonstrate that the proposed KSSICAbased fault detection method can detect the process faults more agilely and obtain a higher fault detection rate than the conventional fault detection method based on dynamic kernel principal component analysis (DKPCA).

Key words: , fault detection； nonlinearity； dynamic characteristic； kernel canonical variate analysis； independent component analysis； fault detection rate

中图分类号:

TP 277

蔡连芳，田学民，张妮. 基于核状态空间ICA的非线性动态过程故障检测方法[J]. 上海交通大学学报（自然版）, 2014, 48(07): 971-976.

CAI Lianfang,TIAN Xuemin,ZHANG Ni. A Nonlinear Dynamic Process Fault Detection Method Based on

Kernel State Space Independent Component Analysis

[J]. Journal of Shanghai Jiaotong University, 2014, 48(07): 971-976.

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基于核状态空间ICA的非线性动态过程故障检测方法

A Nonlinear Dynamic Process Fault Detection Method Based on
Kernel State Space Independent Component Analysis

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