Anomaly Detection and Localization for Process Security Based on the Multivariate Statistical Method

Anomaly detection is very important for system monitoring and security since successful execution of these engineering tasks depends on access to validated data. The localization of the variable causing the fault is very essential. Indeed, the localization of the fault is defined as the ability to determine the source of the fault on a system. Generally, the identification of faults is linked to the detection procedure implemented. Therefore, it is very important to choose the adequate fault detection model to locate fault. For nonlinear uncertain systems, the most performed fault detection method is reduced rank interval kernel principal component analysis (RRIKPCA), which enhances the computational skill by downgrading the kernel matrix dimension. We have proposed in this article a new fault localization technique for uncertain systems, named partial RRIKPCA, which combines the benefits of the RRIKPCA technique and the principle of partial localization. The principal of this method involves selecting partially reduced rank data subsets and then building more accurate models with fewer PCs and isolating faults with higher precision. The proposed fault isolation method is applied for monitoring air quality monitoring network (AIRLOR) data.