Abstract

The process monitoring systems are often utilized in environmental process operations. Many practical applications used for scheduling, planning or operator training are often complex for direct usage in process monitoring. In this paper, it is proposed to use the generalized likelihood ratio (GLR) based principal components analysis (PCA) for process monitoring and fault detection of environmental processes. The objective is to combine the GLR test with PCA model in order to improve the fault detection performance. GLR-based PCA is a multivariate statistical technique used in multivariate statistical process monitoring and fault detection. PCA reduces the dimensionality of the original data by projecting it onto a space with significantly fewer dimensions. It obtains the principal events of variability in a process. If some of these events change, it can be due to a fault in the process. The data are collected from the crop model in order to calculate the PCA model and the thresholds; Hotelling statistic, T², Q statistic and GLR test statistic are used in order to detect the faults. It is demonstrated that the performance of faults detection can be improved by combining GLR test and PCA.