Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine
The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However,...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31925/ http://umpir.ump.edu.my/id/eprint/31925/1/Faysal%202021%20IOP%20COF2.pdf |
| _version_ | 1848823892248887296 |
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| author | Atik, Faysal Adhreena, M S N A Vorathin, E. Hafizi, Z. M. Ngui, W K |
| author_facet | Atik, Faysal Adhreena, M S N A Vorathin, E. Hafizi, Z. M. Ngui, W K |
| author_sort | Atik, Faysal |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However, the pipeline leakages become a major concern as their failure may contribute to operational and economic loss as well as environmental pollution. This paper proposed a system to detect pipe fault at different locations. Empirical Mode Decomposition (EMD) was applied for feature extraction using energy and kurtosis. The one-against-one (OAO) and one-against-all (OAA) multiclass SVM with radial basis function (RBF), polynomial and sigmoid kernel functions were implemented in order to classify the multiple fault locations from the extracted features. RBF kernel function recorded the highest classification accuracy for both OAO and OAA approaches with 97.77% and 96.29%, respectively, followed by slightly reduced accuracy for sigmoid whereas significantly low accuracy for the polynomial kernel. The outputs were further analysed to justify the performance of the classifiers. From all the cases, it was observed that OAO-SVM with RBF kernel performed the best for pipe fault diagnosis. |
| first_indexed | 2025-11-15T03:04:21Z |
| format | Article |
| id | ump-31925 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:04:21Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-319252021-09-01T13:28:20Z http://umpir.ump.edu.my/id/eprint/31925/ Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine Atik, Faysal Adhreena, M S N A Vorathin, E. Hafizi, Z. M. Ngui, W K T Technology (General) TJ Mechanical engineering and machinery The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However, the pipeline leakages become a major concern as their failure may contribute to operational and economic loss as well as environmental pollution. This paper proposed a system to detect pipe fault at different locations. Empirical Mode Decomposition (EMD) was applied for feature extraction using energy and kurtosis. The one-against-one (OAO) and one-against-all (OAA) multiclass SVM with radial basis function (RBF), polynomial and sigmoid kernel functions were implemented in order to classify the multiple fault locations from the extracted features. RBF kernel function recorded the highest classification accuracy for both OAO and OAA approaches with 97.77% and 96.29%, respectively, followed by slightly reduced accuracy for sigmoid whereas significantly low accuracy for the polynomial kernel. The outputs were further analysed to justify the performance of the classifiers. From all the cases, it was observed that OAO-SVM with RBF kernel performed the best for pipe fault diagnosis. IOP Publishing 2021-02-18 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31925/1/Faysal%202021%20IOP%20COF2.pdf Atik, Faysal and Adhreena, M S N A and Vorathin, E. and Hafizi, Z. M. and Ngui, W K (2021) Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine. IOP Conf. Series: Materials Science and Engineering, 1078 (1). pp. 1-9. ISSN 1757-899X (online); 1757-8981 (Print). (Published) https://iopscience.iop.org/issue/1757-899X/1078/1 doi:10.1088/1757-899X/1078/1/012023 |
| spellingShingle | T Technology (General) TJ Mechanical engineering and machinery Atik, Faysal Adhreena, M S N A Vorathin, E. Hafizi, Z. M. Ngui, W K Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title | Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title_full | Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title_fullStr | Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title_full_unstemmed | Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title_short | Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| title_sort | leak diagnosis of pipeline based on empirical mode decomposition and support vector machine |
| topic | T Technology (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/31925/ http://umpir.ump.edu.my/id/eprint/31925/ http://umpir.ump.edu.my/id/eprint/31925/ http://umpir.ump.edu.my/id/eprint/31925/1/Faysal%202021%20IOP%20COF2.pdf |