LAUSR

Abstract Across the globe, pipelines help to carry all kinds of fluids across vast distances. Our prior work in fiber Bragg grating (FBG) hoop strain sensors are among the most recently reported technologies aimed at accomplishing the goal of continuous pipeline monitoring. Multiple hoop strain signals can be extracted from distributed FBG hoop strain sensors set along the pipeline to reflect leakage process. In this paper, we demonstrate the use of multiple, distributed FBG hoop strain sensors in cooperation with a support vector regression (SVR) to localize a leakage point along a model pipeline. Series of terminal hoop strain variations are extracted as the input variables to achieve multi regression analysis as to localize the leakage point. The parameters of different kernel functions are optimized through five-fold cross validation to obtain the highest leakage localization accuracy. The result shows that when taking radial basis kernel function (RBF) with optimized C and γ values, the localization mean square error (MSE) reaches as low as 0.043. The anti-noise capability of the SVR model is evaluated through superimposing Gaussian white noise of different levels. From the simulation study, the average localization error is still acceptable (≈500 m) even in 5% noise situation. The influence of hoop strain sensing points as input variables is also investigated. The system with more hoop strain sensing points shows more stable capability for different level noises. The results demonstrate feasibility and robustness of the SVR approach using multi-hoop strain measurements for pipeline leakage localization.