Abstract
Abstract The Bayesian treed Gaussian method is introduced in this paper to implement process monitoring based on historical data. This method can cover the disturbances in a process and discover differences among individually monitored variables before and after an abnormal situation occurs. The analysis results from the historical values of each variable help to differentiate abnormal from normal states in the process. Here, the Tennessee Eastman process is studied to show the effectiveness of this method for process monitoring.
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