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A novel pipeline leak detection approach independent of prior failure information

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Abstract
Condition monitoring of pipelines is of importance to detect fluids leakage and associated financial losses and
accidents. Artificial intelligence (AI) techniques have been widely used for the pipeline condition assessment. A
major limitation of the currently-used supervised AI methods is that they heavily rely on sufficient pipeline
failure historical data for their training. To cope with this issue, this paper proposes a health index-oriented
approach based on multiscale analysis, Kolmogorov-Smirnov (KS) test, and Gaussian mixture model (GMM)
for determining the leakage situation in pipelines. GMM is an unsupervised AI method capable of training itself
with pipeline normal condition data. In this study, acoustic emission (AE) signals are first acquired from the
pipeline at different pressure conditions. Then, the multiscale analysis and KS test are deployed to extract
suitable features from the AE signals. The feature samples corresponding to the pipeline normal condition are
used to train the GMM. Finally, the feature samples to be tested are supplied to the GMM and the desired health
indicator is obtained. The results confirm the effectiveness of the proposed approach in discriminating the
normal and leak conditions as well as the severity of leaks. Further, the GMM classifier trained with features
derived from multiscale analysis and the KS test outperforms the GMM trained with crest factor, and mean
frequency.
Author(s)
Akhand Rai김종면
Issued Date
2021
Type
Article
Keyword
PipelinesCondition monitoringKolmogorov-Smirnov (KS) testGaussian mixture modelLeak detection
DOI
10.1016/j.measurement.2020.108284
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9033
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_journals_2463688334&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20novel%20pipeline%20leak%20detection%20approach%20independent%20of%20prior%20failure%20information&offset=0&pcAvailability=true
Publisher
MEASUREMENT
Location
영국
Language
영어
ISSN
0263-2241
Citation Volume
167
Citation Number
1
Citation Start Page
108284
Citation End Page
108284
Appears in Collections:
Engineering > IT Convergence
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