基于子块典型变量分析的化工过程故障检测  

Fault Detection in Chemical Process Based on Sub-Block Canonical Variate Analysis

作　　者：郭小萍 赵英平 李元 GUO Xiaoping;ZHAO Yingping;LI Yuan(Shenyang University of Chemical Technology,Shenyang 110142,China)

机构地区：[1]沈阳化工大学信息工程学院,辽宁沈阳110142

出　　处：《沈阳化工大学学报》2024年第1期61-70,共10页Journal of Shenyang University of Chemical Technology

基　　金：国家自然科学基金资助项目(61490701,61673279);辽宁省教育厅重点实验室项目(LZ2015059)。

摘　　要：大型化工生产过程包含许多单元,单元内变量之间具有非常强的相关性,针对这一特点,提出一种基于子块典型变量分析(sub-block canonical variate analysis,SB-CVA)的故障检测方法.依据块内变量高度相关、块间变量相关性较小原则将过程建模数据分块,在各个块内分别建立CVA模型,计算T 2和SPE统计量作为故障检测指标.该方法主要优点是能及时检测故障并定位故障发生的单元.对TE过程进行仿真,并与PLS、MB-PLS和CVA方法进行对比,结果验证了该方法的有效性.The large-scale chemical production process contains many units,and the variables within the units have a very strong correlation.In view of this characteristic,a fault detection method based on sub-block canonical variate analysis(SB-CVA)was proposed.Firstly,the process modeling data were divided into several sub-blocks according to the principle of high correlation of intra-block variables and low correlation of inter-block variables.Then CVA models were established within each block,T 2 and SPE statistics were calculated as fault detection indicators.The proposed method could detect the fault of sub-blocks in time and confirm the fault unit effectively.Simulation was carried out through TE process and compared with PLS,MB-PLS and CVA to verify the effectiveness of the proposed method.

关 键 词：子块 典型变量分析 故障检测 TE过程 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]