基于二维主元分析的间歇过程故障诊断

传统的多向主元分析(MPCA)已广泛应用于监视多变量间歇过程。在MPCA算法中，三维的间歇过程数据需要转换为高维的二维向量，导致计算量和存储空间大，同时不可避免地丢失一些重要信息。因此，提出一种新的基于二维主元分析(2DPCA)的故障诊断方法。由于每个批次的间歇过程数据是一个二维向量(矩阵)，应用以各个批次矩阵为分析对象的2DPCA算法，避免矢量化，存储空间和存储需求小;另外，2DPCA采用各个批次的协方差的平均值来进行建模，能够更加准确地反映出不同类型的故障，在一定程度上增强了故障诊断的准确性。半导体工业实例的监视结果说明，2DPCA方法优于MPCA。

Fault diagnosis for batch processes based on two-dimensional principal component analysis

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.