WML: Wireless Sensor Network based Machine Learning for Leakage Detection and Size Estimation

• DOI: 10.1016/j.procs.2015.08.329
• Publication Date: 2015-09-15T03:53:04Z
• Authors: Sidra Rashid; Usman Akram; Shoab A. Khan
• Keywords: leakage detection and size estimation; 0208 environmental biotechnology; 02 engineering and technology; Wireless sensor network based machine learning(WML); Negative pressure wave (NPW)

Fluid (oil/gas/water) transportation systems present a significant challenge for pipeline health monitoring. With the development of smart devices capable micro-sensing, on-board processing, and wireless communication capabilities, sensor networks are able to facilitate online learning reliable event monitoring reporting distribution pipelines. This paper presents design, testing network (WSN) leak detection size estimation in long range system uses machine (WML) learn, make decisions report critical events like slow /small leakages natural gas/oil autonomously. Machine is performed on negative pressure wave (NPW) identify based raw data gathered by individual nodes network. In learning, we use support vector (SVM), K-nearest neighbor (KNN) Gaussian mixture model (GMM) Naive bayes multi- dimensional feature space. The proposed technique investigated performance capabilities series trials field deployed test bed, with regard leakage