Local component based principal component analysis model for multimode process monitoring

Abstract

In order to achieve improved the performance of conventional principal component analysis in multimode processes, we have investigated the local component to eliminate the impact of multimode properties. • LCPCA can eliminate the impact of multimode features on the conventional PCA method. • LCPCA can automatically identify the number of the process operating conditions. • LCPCA can provide suitable data foundation for monitoring process. For plant-wide processes with multiple operating conditions, the multimode feature imposes some challenges to conventional monitoring techniques. Hence, to solve this problem, this paper provides a novel local component based principal component analysis (LCPCA) approach for monitoring the status of a multimode process. In LCPCA, the process prior knowledge of mode division is not required and it purely based on the process data. Firstly, LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture (FGMM). Then, calculating the posterior probability is applied to determine each sample belonging to which local component. After that, the local component information (such as mean and standard deviation) is used to standardize each sample of local component. Finally, the standardized samples of each local component are combined to train PCA monitoring model. Based on the PCA monitoring model, two monitoring statistics T 2 and SPE are used for monitoring multimode processes. Through a numerical example and the Tennessee Eastman (TE) process, the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.