基于全变量信息的子空间监控方法

Subspace monitoring based on full variable information

实际化工过程采集得到的数据往往维度较高,直接建模比较复杂。主元分析(principal component analysis,PCA)方法可以提取原始数据主要特征,得到低维数据,但传统的PCA过程监控方法仅保留了方差较大的主元,会造成信息缺失,这将大大影响过程监控性能。针对这一问题,提出了一种新的基于全变量信息(full variable information,FVI)的子空间监控方法。首先,依据每个变量与主元空间(principal component subspace,PCS)和残差空间(residual subspace,RS)相似性的高低,将原始数据空间划分为3个维度较低的子空间,3个子空间保存了全部过程变量,可以更充分地利用过程信息。其次,在每个子空间中,分别建立监控模型,并利用贝叶斯推断整合子空间的监控结果。最后,通过数值仿真及Tennessee Eastman(TE)过程仿真研究验证FVI方法的有效性。

Since data collected from chemical processes always have high dimensions, modeling directly can be very complex. PCA can extract main features of the original data and obtain a more compact representation. However, the traditional PCA process monitoring scheme may cause information loss, since it only preserves principal components with large variance, which will greatly affect the performance of process monitoring. To handle this problem, a novel subspace monitoring method based on full variable information was proposed. Firstly, Based on the similarity level between each variable and principal component subspace（PCS） or residual subspace（RS）, the original data space was divided into three low-dimensional subspaces, which preserved the whole process variables. Thus it could use the process information better. Secondly, the monitoring models were established respectively in each subspace, and then the Bayesian inference was introduced to integrate monitoring results of the subspaces. Finally, the feasibility and effectiveness of the FVI method were illustrated through a numerical example and the Tennessee Eastman process.