Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Abstract

Summary A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.

On the use of acquisition function‐based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection

“…Several metaheuristic optimization approaches (genetic algorithm and particle swarm optimization [PSO]) are recently proposed in the SHM literature to resolve these issues to some extent 19,20 . PSO is one of the most commonly used metaheuristic techniques in SHM literature 20–23 As PSO can be used for black box optimization as well, recently, Gui et al 20 utilized the PSO technique for optimal selection of hyperparameters in SVM for achieving better accuracy of damage detection. Another recent use of PSO in SHM is by Jia et al 21 ; in this work PSO has been utilized to tune the hyperparameters of a support vector regression model for damage localization in pipelines.…”

Pipeline leakage monitoring experiments based on evaporation‐enhanced FBG temperature sensing technology

“…Optical fiber sensing technology has the advantages of high measurement accuracy, anti‐electromagnetic interference, long‐term stability, high security, and distributed real‐time online monitoring 26–29 . It has great application potential in pipeline leakage monitoring and has become a research hot spot 30–33 . Optical fiber sensing technology realizes leakage monitoring by measuring the physical parameters of pipelines such as pipeline temperature, 34–36 strain, 37,38 and vibration 39 .…”

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

“…From the perspective of measurement signal acquisition, pipeline leak detection and positioning systems can be broadly classified into external detection methods and internal detection methods [2]. Externally-based methods monitor external pipeline parameters, such as acoustic signals [9][10][11] and fiber-optic cables [12][13][14], while internally-based methods typically collect pressure, flow, and temperature signals, such as real time transient modeling [15][16][17][18], negative pressure wave method [19,20], pressure point analysis, and the flow balance method. Recently, some scholars have also pointed out that integrating multi-source signals, including internal sensors and external sensors, is also an effective way to improve the performance of pipeline leak detection and localization [21].…”

“…Recently, Wu and Liu [8] presented a detailed review on data-driven approaches for leak detection (specifically, burst detection) in the water distribution. Typically, artificial neural network-based methods [11,27,28] and support vector machine-based methods [13,[28][29][30] are the most widely used data-driven approaches for pipeline detection and localization. Besides these, there are other data-driven methods for pipeline leak detection, such as genetic algorithm [31], principal component analysis (PCA) [31], particle swarm optimization [12], support vector data description (SVDD) [32], and Bayesian reasoning [33,34].…”

“…The experimental results showed that the particle swarm optimization has strong global search ability when optimizing support vector machine parameters, which further improves the accuracy of leak detection. Similarly, the particle swarm optimization algorithm was used to optimize the parameters of the kernel functions of support vector machine and support vector regression in Jia et al [13], in which the support vector machine was used for pipeline leak detection and support vector regression for pipeline leak localization. Mandal et al [37] proposed a leak detection approach based on the rough set theory and support vector machine to improve leak detection accuracy.…”