Abstract:

Principal component analysis (PCA), a classical algorithm for feature extraction, has been widely used in multivariate process monitoring. Conventional PCA selects those principal components with larger variance in order to maintain more information of modeling samples. However, when process information is changed, principal components with smaller variance may exhibit more obvious transformation, which means they are more informative and more beneficial for fault detection. Hence, a new process monitoring method was proposed on a basis of informative principal component subspace reconstruction (Info-PCA). Info-PCA calculated change rates of cumulative T² of process data in different directions of principal components and reconstructed a principal component subspace by selecting those components with larger change rates. Then, a statistical process monitoring model was built. Finally, feasibility and validity of the Info-PCA monitoring method were demonstrated by a case study of a chemical process.

Key words: algorithm, computer simulation, principal component analysis, fault detection, subspace reconstruction

摘要：

作为一种经典的多元投影方法，主元分析（PCA）已在多变量统计过程监测领域得到了广泛应用。然而，传统的主元挑选方法往往选择方差较大的主元以表征建模样本中包含的较大信息量，但当过程信息发生变化时，方差较小的主元所表现出来的变异性可能更为明显，即包含的信息量更为丰富，也更有利于故障检出。为此，提出一种基于主元子空间富信息重构的过程监测方法（informative PCA，Info-PCA）。该方法通过计算过程数据在各主元方向上累积T²统计量的变化率，选择变化较为明显的主元以重构主元子空间。在此基础上，建立相应的统计监测模型。最后，通过实例验证该方法用于过程监测的可行性与有效性。

关键词: 算法, 计算机模拟, 主元分析, 故障检测, 子空间重构