基于多层优化PCC-SDG方法的化工过程故障诊断

化工过程的故障发生往往都是一个变量带动多个变量的连锁效应。本文基于变量的相关性变化特点，用符号有向图SDG（signed directed graph）描述系统因果影响关系，以皮尔逊相关系数PCC（Pearson correlation coefficient）计算网络统计指标，提出了一种基于多层优化PCC-SDG的故障诊断方法。该方法基于全工艺的网络拓扑结构，首先对选取的变量进行初步优化。然后，为有效提取工艺特征信息，运用PCA（principal component analysis）权重思想从多层相关系数集中选取了权重较大的关键变量，结合SDG建立最优PCC-SDG网络。最后，针对最优PCC-SDG网络变量的相关性规律重构聚集权重系数Q，进行过程故障检测与诊断。TE（Tennessee Eastman）仿真过程的应用结果表明，PCC-SDG建模及故障诊断步骤较为简洁，可以充分挖掘SDG深层次关联特性，定量简化SDG的故障诊断效果明显，具有较好的过程监控优势。

A novel fault diagnosis method based on multilayer optimized PCC-SDG

Chemical process failures are often caused by a series of variables with a chain effect. This study utilizes variable correlation characteristics, PCC (Pearson correlation coefficient) statistical index, and SDG (signed directed graph) to describe the causal relationship among variables, and then proposes a PCC-SDG fault diagnosis method based on a multi-layer optimization structure. With the topological network structure of the whole process as reference, this method first performs an initial optimization on the selected variable. An optimal PCC-SDG network is then constructed on the specific variables which have large PCA (principal component analysis) weights in the multilayer correlation coefficient set. After that, the rule of gather weighting coefficient Q is established to identify process fault. The application on Tennessee Eastman process illustrates that the PCC-SDG method can realize fault detection and isolation tasks in an effective pattern. Because its modeling and diagnosis procedures are simple and SDG can be readily probed for the root cause, the proposed method has an advantage in process supervision.