Fault diagnosis model of beer fermentation process based on multiway kernel principal component analysis

摘要

Aiming at the limitation of the application of principal component analysis model for fault diagnosis in nonlinear time-varying process, kernel transformation theory is introduced into the data feature extraction of nonlinear space, on the basis of the periodic characteristics of the batch process, putting forward a kind of improved multi-way kernel principal component analysis fault diagnosis model, which effectively solves the nonlinear problem of process data and ensures integrity of data and information extraction. By comparing with other methods in experiment, the results show that the proposed method has good real-timing and accuracy to slow time-varying of batch process.