关键词: 间歇过程, 故障诊断, 主元分析, 多向主元分析, 二维主元分析

Abstract: Multiway Principal Component Analysis (MPCA) has been widely used to monitor multivariate batch process. In MPCA method, the batch data are transformed as a vector in high-dimensional space, resulting in large computation, storage space and loss of important information inevitably. A new batch process fault diagnosis method based on the two-Dimensional Principal Component Analysis (2DPCA) was presented. Essentially, every batch data was presented as a second order vector, or a matrix. In this case, 2DPCA could be used to deal with the two-dimensional batch data matrix directly instead of performing vectorizing procedure with low memory and storage requirements. In addition, 2DPCA was used to model with the covariance average of all the batches, which accurately reflected the different faults and enhanced the accuracy of fault diagnosis to a certain extent. The monitoring results of an industrial example show that the 2DPCA method outperforms the conventional MPCA.

Key words: batch process, fault diagnosis, Principal Component Analysis (PCA), Multiway Principal Component Analysis (MPCA), two-Dimensional Principal Component Analysis (2DPCA)