A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model
Title
A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model
Subject
Gaussian mixture model
Gradient and slope turns rejection
Leak detection
Pipeline
Description
This paper presents a novel pipeline leak detection scheme based on gradient and slope turns rejection (GSTR). Instead of monitoring the pipeline under constant working pressure, GSTR introduces a new testing method which obtains data during the transient periods of different working pressures. A novel pipeline leak detection method based on those transient data without failure history is proposed. Wavelet packet analysis (WPA) is applied to extract features which capture the dynamic characteristics from the non-stationary pressure data. Principal component analysis (PCA) is used to reduce the dimension of the feature space. Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components. Bayesian information criterion (BIC) is used to determine the number of mixtures for the GMM and a density boosting method is applied to achieve better accuracy of the distribution estimation. An experimental case study for oil pipeline system is used as an example to validate the effectiveness of the proposed method.
60-69
1
25
Creator
Liang, Wei
Zhang, Laibin
Publisher
Journal of Loss Prevention in the Process Industries
Date
2012
Type
journalArticle
Identifier
0950-4230
10.1016/j.jlp.2011.06.017
URL
https://www.sciencedirect.com/science/article/pii/S0950423011000982
Collection
Citation
Liang, Wei and Zhang, Laibin, “A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model,” Lamar University Midstream Center Research, accessed May 14, 2024, https://lumc.omeka.net/items/show/26896.