Multiscale Gaussian process regression-based generalized likelihood ratio test for fault detection in water distribution networks

This paper proposes a new leak/contaminant detection approach that aims to enhance the monitoring of water distribution network (WDN). The developed method relies on using machine learning (e.g Gaussian process regression (GPR)) as a modeling framework and generalized likelihood ratio (GLRT) for detection purposes. To improve the performances of the developed GPR model even further, multiscale representation of data will be used to develop multiscale extension of these method. Multiscale representation is a powerful data analysis technique that presents efficient separation of deterministic characteristics from random noise. Therefore, the multiscale GPR method, that combines the advantages of the GPR method with those of multiscale representation, will be developed to enhance the WDN modeling performance. We develop a new technique for detecting leak/contaminant in WDN using GLRT. For further enhance, the performance of GLRT, an exponentially weighted moving average (EWMA)-GLRT (EWMA-GLRT) chart is developed. The simulation results show that the MSGPR-based EWMA-GLRT method outperforms MSGPR-based GLRT and that both of them provide clear advantages over the neural networks (NN)- and support vector regression (SVR)- and GPR-based GLRT techniques.