For plant-wide processes with multiple operating conditions

the multimode feature imposes some chal lenges 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 pro cesses. 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.