@article{ref1,
title="A model-based health indicator for leak detection in gas pipeline systems",
journal="Measurement",
year="2021",
author="Xiao, Rui and Hu, Qunfang and Li, Jie",
volume="171",
number="",
pages="e108843-e108843",
abstract="Leakage in gas pipelines is becoming a significant issue and has attracted much attention in recent years. This paper is concerned with the development of a robust health indicator for identifying the leakage in gas pipeline systems. A spectral exponent indicator is proposed based on a theoretical leak noise spectrum model. Measurements of the leak acoustic signals are also presented from a pipe rig with air under pressure. Then, a feature selection technique is employed to select properly desired features. Three data-driven approaches, artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF) are trained with the most discriminative features. The proposed methodology showed to achieve 99.4%, 99.6% and 99.4% accuracies for ANN, SVM and RF respectively. Furthermore, the proposed indicator showed to be robust under different conditions illustrating its ability for applications in the field. © 2020 Elsevier   Keywords: Pipeline transportation <p /> <p>Language: en</p>",
language="en",
issn="0263-2241",
doi="10.1016/j.measurement.2020.108843",
url="http://dx.doi.org/10.1016/j.measurement.2020.108843"
}