东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (2): 173-177.DOI: -

• 论著 • 上一篇    下一篇

控制过程异常数据的在线检测

刘芳;毛志忠;   

  1. 东北大学信息科学与工程学院;东北大学流程工业综合自动化国家重点实验室;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-01-17
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2007AA04Z194,2007AA041401)

Online detection of outliers of control process data

Liu, Fang (1); Mao, Zhi-Zhong (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-01-17
  • Contact: Liu, F.
  • About author:-
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摘要： 针对传统小波方法在检测异常数据方面的不足及控制系统中过程数据的特点,提出了一种适用于工业控制系统的异常过程数据在线检测方法.此方法采用基于模型的小波分析残差的检测思想,考虑异常值对模型的影响,提出了带有反馈结构的RBF网络模型,从而有效降低了异常点对RBF网络准确性的影响,提高了网络的鲁棒性;采用隐马尔可夫模型分析小波系数,避免了检测阈值的设定.实验与应用证明了该检测算法比传统小波检测算法更适合于控制过程异常数据的检测.

关键词: 异常数据检测, 径向基函数, 小波, 隐马尔可夫模型, 在线检测, 过程数据

Abstract: A new method for detecting the outliers of control process data is proposed to compensate the deficiencies of the conventional wavelet methods. A wavelet method is used to decompose the fitting error of the output and its estimate, and a RBF model with backfeed structure is developed to reduce the effect of the outliers on the precision of the RBF network, thus improving the robustness of the network. HMM is introduced into the analysis of the wavelet coefficents, which can recognize the outliers without presetting the detection threshold. Experiment and application show the validity of the proposed method.

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