Fault Diagnosis for Batch Processes Based on 2DPCA

Multiway Principal Component Analysis (MPCA) has been widely used to monitor multivariate batch process. In MPCA method, the batch data is represented 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 2-Dimensional Principal Component Analysis (2DPCA) is presented. Essentially, a batch data is presented as a second order vector, or a matrix. In this case, 2DPCA may be used to deal with the two-dimensional batch data matrix directly instead of performing vectorizing procedure. Furthermore, 2DPCA has some advantages such as low memory and storage requirements. On the other hand, 2DPCA is used to model for the covariance average of all the batches, thus it more accurately reflects the fault at different times and enhances the accuracy of fault diagnosis to a certain extent. The monitoring results of Reactor of Thermal Anneal (RTA) batch process show that the 2DPCA method outperforms the conventional MPCA.