上海交通大学学报

上海交通大学学报（自然版） ›› 2014, Vol. 48 ›› Issue (07): 1004-1008.

• 自动化技术、计算机技术 • 上一篇    下一篇

基于双核独立元分析的化工过程故障诊断算法研究

赵小强1,2,钱君秀1
  

  1. (1.兰州理工大学电气工程与信息工程学院，兰州 730050;2.甘肃省制造业信息化工程技术研究中心，兰州 730050)
     
     
     
  • 收稿日期:2013-06-25 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（61005026），甘肃省高校基本科研业务费项目（1203ZTC061），甘肃省制造业信息化工程技术研究中心开放基金资助(2012MIE01F02)

A Fault Diagnosis Algorithm for Chemical Process Based on Dual-Kernel Independent Component Analysis

ZHAO Xiaoqiang1,2,QIAN Junxiu1
  

  1. (1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China； 2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 73005, China)
     
  • Received:2013-06-25 Online:2014-07-28 Published:2014-07-28

摘要：

由于化工生产过程数据具有强非线性和非高斯性特征，提出了核主元分析与核独立元分析相结合的可用于化工过程故障诊断的双核独立元分析算法，该算法利用核主元分析的非线性核函数把数据从原空间映射到高维特征空间进行白化预处理，再用核独立元分析算法进行独立元分析，在特征空间中获得故障监控统计量，计算控制置信限，达到有效的故障诊断. 提出的算法应用在连续搅拌反应釜过程中，结果表明，该算法对化工过程故障诊断能有效提高准确度、降低漏报率和误报率.
 

关键词: 化工过程, 故障诊断, 双核独立元分析, 连续搅拌反应釜过程

Abstract:

A dual-kernel independent component analysis (DKICA) algorithm for chemical process fault diagnosis based on kernel principal component analysis (KPCA) and kernel independent component analysis（KICA） was proposed. First, this algorithm uses nonlinear kernel function of KPCA to whiten preprocessing data by mapping the original space into the high-dimension feature space. Then, the KICA algorithm deals with the data while statistical indices of fault monitoring are obtained and control confidence limits are calculated in the feature space. The proposed algorithm was applied to the continuous stirred tank reactor (CSTR) process. The results indicate that the algorithm can effectively increase the accuracy and reduce the false negative rate and false positive rate of fault diagnosis for nonlinear chemical process.
 

Key words: chemical process, fault diagnosis, dual-kernel independent component analysis (DKICA), continuous stirred tank reactor(CSTR) process

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