Nonlinear fermentation process fault monitoring based on JITL strategy

PCA, as the representative of the traditional global fault monitoring algorithm, is a linear feature extraction algorithm. So global algorithm may cause a lot of false alarms in fault monitoring of fermentation process with nonlinear and multi-stage characteristics. In this paper, the Just In Time Learning (JITL) strategy is introduced into the Kernel Principal Component Analysis (KPCA) algorithm. The local model is established with the data similar to the current online sample. Because the local model can represent the current state of the system, it is not necessary to identify the stages before monitoring. At the same time, the local KPCA model can be used to feature extract nonlinear data. The data generated by the PenSimv2.0 simulation platform is used for verifying the algorithm. The results show that this method has a better effect than KPCA algorithms.