A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System
This paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, wh...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/8/1472 |
| _version_ | 1798024518016434176 |
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| author | Thang Bui Quy Sohaib Muhammad Jong-Myon Kim |
| author_facet | Thang Bui Quy Sohaib Muhammad Jong-Myon Kim |
| author_sort | Thang Bui Quy |
| collection | DOAJ |
| description | This paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, which contains the symptoms of a leak and keeps its characteristic adequately stable even when the environmental conditions change, is calculated. Finally, a k-nearest neighbor (KNN) classifier is trained using features extracted from the transformed signals to identify leaks in the pipeline. Experiments are conducted under different conditions to confirm the effectiveness of the proposed method. The results of the study indicate that this method offers better quality and more reliability than using features extracted directly from the AE signals to train the KNN classifier. Moreover, the proposed method requires less training data than existing techniques. The transformation method is highly accurate and works well even when only a small amount of data is used to train the classifier, whereas the direct AE-based method returns misclassifications in some cases. In addition, robustness is also tested by adding Gaussian noise to the AE signals. The proposed method is more resistant to noise than the direct AE-based method. |
| first_indexed | 2024-04-11T18:04:58Z |
| format | Article |
| id | doaj.art-12ce19e6417048568a4d0f8678e76994 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:04:58Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-12ce19e6417048568a4d0f8678e769942022-12-22T04:10:22ZengMDPI AGEnergies1996-10732019-04-01128147210.3390/en12081472en12081472A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline SystemThang Bui Quy0Sohaib Muhammad1Jong-Myon Kim2School of Computer Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Computer Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Computer Engineering, University of Ulsan, Ulsan 44610, KoreaThis paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, which contains the symptoms of a leak and keeps its characteristic adequately stable even when the environmental conditions change, is calculated. Finally, a k-nearest neighbor (KNN) classifier is trained using features extracted from the transformed signals to identify leaks in the pipeline. Experiments are conducted under different conditions to confirm the effectiveness of the proposed method. The results of the study indicate that this method offers better quality and more reliability than using features extracted directly from the AE signals to train the KNN classifier. Moreover, the proposed method requires less training data than existing techniques. The transformation method is highly accurate and works well even when only a small amount of data is used to train the classifier, whereas the direct AE-based method returns misclassifications in some cases. In addition, robustness is also tested by adding Gaussian noise to the AE signals. The proposed method is more resistant to noise than the direct AE-based method.https://www.mdpi.com/1996-1073/12/8/1472acoustic emissionsk-nearest neighborleak detectionpipeline diagnosticsreliability |
| spellingShingle | Thang Bui Quy Sohaib Muhammad Jong-Myon Kim A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System Energies acoustic emissions k-nearest neighbor leak detection pipeline diagnostics reliability |
| title | A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System |
| title_full | A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System |
| title_fullStr | A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System |
| title_full_unstemmed | A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System |
| title_short | A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System |
| title_sort | reliable acoustic emission based technique for the detection of a small leak in a pipeline system |
| topic | acoustic emissions k-nearest neighbor leak detection pipeline diagnostics reliability |
| url | https://www.mdpi.com/1996-1073/12/8/1472 |
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