Fault Localization in Batch Processes through Progressive Principal Component Analysis Modeling

Abstract

A technique for fault localization in batch processes using progressive principal component analysis (PCA) modeling is proposed in this paper. A PCA model is developed from normal process operation data and is used for online process monitoring. Once a fault is detected by the PCA model, process variables that are related to the fault are identified using contribution analysis. The time information on when abnormalities occurred in these variables is identified using a time series plot of the squared prediction errors (SPE) on these variables. These variables are then removed and another PCA model is developed using the remaining variables. If the faulty batch cannot be detected by the new PCA model, then the remaining variables are not related to the fault. If the faulty batch can still be detected by the new PCA model, then further variables associated with the fault are identified from SPE contribution analysis. The procedure is repeated until the faulty batch can no longer be detected using the remaining variables. Using the time information on when abnormalities presented in the variables associated with the fault, fault propagation paths can be established and the origin of the fault could be traced. The proposed method is tested on a benchmark simulated fed-batch penicillin production process, PenSim. The results demonstrate that the proposed method is particularly effective in isolating faults that have occurred on measured variables. For more complex faults that have occurred on unmeasured variables, the method can identify variables affected by the fault, and process knowledge is required to determine the fault.