上海交通大学学报

上海交通大学学报（自然版） ›› 2011, Vol. 45 ›› Issue (08): 1202-1206.

• 自动化技术、计算机技术 • 上一篇    下一篇

基于等距离映射的非线性动态故障检测方法

张妮，田学民   

  1. （中国石油大学（华东） 信息与控制工程学院， 山东 东营 257061）
  • 出版日期:2011-08-30 发布日期:2011-08-30
  • 基金资助:

    国家高技术研究发展计划（863）项目（2007AA04Z193）；山东省自然科学基金资助项目（Y2007G49）

Nonlinear Dynamic Fault Detection Method Based on Isometric Mapping

 ZHANG  Ni, TIAN  Xue-Min   

  1. (College of Information and Control Engineering, China University of Petroleum,
    Dongying 257061, Shandong, China)
  • Online:2011-08-30 Published:2011-08-30

摘要： 针对化工过程数据强非线性和动态性的特点，提出了一种基于动态等距离映射(Dynamic Isometric Mapping,DISOMAP)流形学习的非线性过程故障检测方法.该方法首先采用DISOMAP算法提取训练样本的子流形特征，自适应学习近邻点参数，保留了采样数据的流形结构，然后运用线性回归方法得到原空间和降维子流形空间的投影映射，从而将观测数据从原高维空间映射到低维嵌入空间，最后在变换后的低维空间构造统计量T2和SPE进行监控.TE过程的仿真结果表明，所提出的DISOMAP故障检测方法可以比核主元分析(Kernel Principle Component Analysis,KPCA)更为有效地监控过程变化，检测到故障的发生.

关键词: 动态等距离映射, 流形学习, 非线性, 故障检测

Abstract: The data collected from chemical process are strongly nonlinear and dynamic related. To solve this problem, a nonlinear dynamic fault detection method using dynamic isometric mapping (DISOMAP) manifold learning was proposed. It first extracts submanifold feature from original data set with adaptive neighbor parameters, which preserves geometric structure. Then linear regression projection mapping which maps the original high dimension space to a low dimension embedding space is used. Finally, T2 and SPE statistics are constructed in the process monitoring application. The simulation results of Tennessee Eastman process show that DISOMAP-based method is more effective than KPCA (kernel principal component analysis) for process monitoring and fault detection.

Key words: dynamic isometric mapping (DISOMAP), manifold learning, non-linear, fault detection

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