应用阶数自学习自回归隐马尔可夫模型对控制过程异常数据的在线检测
On-line detection of outliers in control process data based on autoregressive hidden Markov model with order self-learning
摘要点击 2124  全文点击 1503  投稿时间:2009-10-15  修订日期:2010-06-07
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DOI编号  10.7641/j.issn.1000-8152.2011.5.CCTA091306
  2011,28(5):631-638
中文关键词  自回归隐马尔科夫模型  BDT  异常数据检测  在线检测
英文关键词  ARHMM model  BDT  outlier detection  online detection
基金项目  国家高新技术研究“863”发展计划资助项目(2007AA04Z194, 2007AA041401).
作者单位E-mail
刘芳 东北大学 信息科学与工程学院 liufang19830311@163.com 
毛志忠 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教育部重点实验室 
 
中文摘要
      针对过程工业中强噪声环境下实时采集的控制过程海量数据难以在线精确检测的问题, 提出了基于阶数自学习自回归隐马尔可夫模型(ARHMM)的工业控制过程异常数据在线检测方法. 该算法采用自回归(AR)模型对时间序列进行拟合, 利用隐马尔科夫模型(HMM)作为数据检测的工具, 避免了传统检测方法中需要预先设定检测阈值的问题, 并将传统的BDT(Brockwell-Dahlhaus-Trindade)算法改进成为对于时间和阶数均实施迭代的双重迭代结构, 以实现ARHMM参数在线更新. 为了减小异常数据对ARHMM参数更新的影响, 本文采用先检测后更新的方式, 根据检测结果采取不同的更新方法, 提高了该算法的鲁棒性. 模型数据仿真与应用试验结果证明, 该算法具有较高的检测精度和抗干扰能力, 同时具备在线检测的能力. 通过与传统基于AR模型的异常数据检测方法比较, 证明了该方法更适合作为过程工业控制过程数据的异常检测工具.
英文摘要
      For the accurate online detection and collection of massive real-time data of a control process in strong noise environment, we propose an autoregressive hidden Markov model (ARHMM) algorithm with order self-learning. This algorithm employs an AR model to fit the time series and makes use of the hidden Markov model as the basic detection tool for avoiding the deficiency in presetting the threshold in traditional detection methods. In order to update the parameters of ARHMM online, we adopt the improved traditional BDT(Brockwell-Dahlhaus-Trindade) algorithm with double iterative structures, in which the iterative calculations are performed respectively for both time and order. To reduce the influence of outlier on parameter updating in ARHMM, we adopt the strategy of detection-before-update, and select the method for updating based on the detection results. This strategy improves the robustness of the algorithm. Simulation with emulation data and practical application verify the accuracy, the robustness and the property of online detection of this algorithm. Comparison between the traditional AR-model-based algorithm and the proposed algorithm shows the superiority of the proposed algorithm in outlier detection in industrial control processes.