A statistical analysis on the leak detection performance of underground and overground pipelines with wireless sensor networks through the maximum likelihood ratio test

Leaks in pipelines of the oil and gas industry are an economic and environmental problem that needs to be detected early and effectively. Wireless sensor networks (WSNs) have been researched as one of those technologies to be used in the remote monitoring of pipeline infrastructure. The idea of using tiny sensor nodes on pipelines seemingly provides industries with effective and reliable real-time monitoring, and better coverage density per area. The benefits are apparent in the deployment of WSNs for pipeline monitoring. However, what really lacks is an actual comparison in the detection performance between deployment in overground pipelines and underground pipelines. Extensive research has been going on the use of wireless underground sensor networks for a number of applications. This paper attempts to provide a statistical insight on the concepts of leak detection performance of WSNs when deployed on overground and underground pipelines. The approach in the study employs the hypothesis testing problem to formulate a solution on the detection plan. Through the hypothesis test, the maximum likelihood ratio scheme is used to provide an optimal performance analysis of the detection idea. The test also takes into consideration the signal to noise ratio performance of the two settings of underground and overground and is crucial in bringing up a conjecture on the performance of detection. As would be shown in the paper, thresholds, determined by probability, are the key in ensuring a good detecting performance for the WSN.