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

Imen Hamrouni,Hajer Lahdhiri,Khaoula Ben Abdellafou,Okba Taouali,Kais Bouzrara,Ahamed Aljuhani,Abdesselem Boulkroune

doi:10.1155/2022/5580774

Abstract

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.

Highlights

Industrial processes are uncertain, caused by the imprecision of measurement; for more precision, data with incertitude became an interval-valued form [1]
In the case of interval-valued data, there are limited available recent methods for dimensionality diminution with interval-valued data-based KPCA approach (IKPCA); the reduced interval kernel principal component analysis (RIKPCA) [6], and the interval reduced rank KPCA based on kernel generalized likelihood ratio test (IRR-KGLRT) [7]
Ese majors fault localization treat only to certain systems; to deal with this limitation, the key objective of this study is to develop a new fault detection and localization approach applied to the nonlinear uncertain process that is capable to treat data learning characterized by nonlinear uncertain large datasets, which is called partial reduced rank IKPCA

Summary

Introduction

Industrial processes are uncertain, caused by the imprecision of measurement; for more precision, data with incertitude became an interval-valued form [1]. For this reason, many researchers deal with this problem of uncertainties, and several linear PCA models for interval-valued data are presented in literature [2,3,4]. Ough in the nonlinear situation, author in [5] proposed the nonlinear interval-valued data-based KPCA approach (IKPCA). Some techniques based on PCA have been proposed in the literature, like localization of defects by PCA which is the approach of contribution diagrams [8]. Unlike the reconstruction approach [9], the contribution approach does not require any information on the defect to generate the plots.

Objectives

Methods

Conclusion