Online statistical hypothesis test for leak detection in water distribution networks

This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time.Peer ReviewedPostprint (author's final draft

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries