Abstract:

For modern large-scale complex dynamic processes, different measured variables have their own serial correlations and interactions among these variables show on different time points. A mutual information based dynamic fault detection method was proposed by an advantageously decentralized modeling strategy. After made multiple time-delayed observations on each variable, the relevant measurements for the variable were separated from all observations by utilizing mutual information and corresponding variable sub-blocks were created. This approach of variable grouping allowed each variable sub-block to capture sufficient information about its own self-and inter-correlations such that the dynamic characteristics of process data could be well analyzed. The principal component analysis (PCA) algorithm was employed to construct statistical modeling on each variable sub-block and a decentralized dynamic fault detection model for large-scale dynamic process. The feasibility and effectiveness of the proposed method on dynamic process modeling were validated by a case study of a chemical process.

Key words: principal component analysis, process systems, mutual information, fault detection, statistical process monitoring

摘要：

对现代大型复杂动态过程来讲，不同测量变量会存在不同的序列相关性，而且变量间的相互影响会体现在不同的采样时刻上。为此，结合利用分散式建模的优势，提出一种基于互信息的分散式动态过程故障检测方法。该方法在对每个测量变量都引入多个延时测量值后，利用互信息为每个变量区分出与其相关的测量值，并建立起相应的变量子块。这种变量分块方式使每个变量子块都能充分地获取与之相对应的自相关性与交叉相关性信息，较好地处理了数据的动态性问题。然后，利用主元分析（PCA）算法对每一变量子块进行统计建模从而建立起适于大规模动态过程的多模块化的故障检测模型。最后，通过实例验证该方法用于动态过程监测的可行性和有效性。

关键词: 主元分析, 过程系统, 互信息, 故障检测, 统计过程监测