Fault Monitoring Approach for Fermentation Process Based on Ensemble Learning

摘要

Due to the complex dynamic behavior in fermentation process, real-time online fault monitoring is very difficult. In this paper, an ensemble learning method, based on a statistical model and a mechanism model, is presented to monitor the fault. First, the linear and nonlinear information which have great effect on fault monitoring are extract by principal component analysis (PCA) and kernel entropy component analysis (KECA). Second, the judgment conditions of faulty information were determined by the dynamic parameter change information of the mechanism model. Third, Bayesian inference is used to transform the monitoring statistics into fault probabilities to integrate the monitoring statistics. Finally, based on the data in a real fermentation process, simulation experiments are carried out. The results show that the monitor model using the ensemble learning has better monitor accuracy than some other methods.