Monitoring based on improved OFA-MPCA
基于改进的OFA-MPCA的监控方法

Multiway Principal Component Analysis (MPCA) is a multivariable statistical approach, which can extract several principal components from the numerous of data to express the data information well, and is mainly used in batch process. In practice, for many reasons, the runtime of each batch is different from others so that the effective statistical model can not be built directly. Orthonormal Function Approximation (OFA) is a technique of project transformation based on orthonormal base, after OFA we can use the projection coefficient to express the characteristics of the original data and synchronize the trajectories of each historical batch and reduce the dimension. This paper presents some improvement on the OFA and combined the MPCA to model and monitor the typical batch process——Penicillin fermentation process. The simulation results show that the improved OFA can deal with data more quickly and the improved OFA-MPCA is able to synchronize the trajectories of all the batches, and monitor the batches perfectly.