The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data
Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/9/2542 |
| _version_ | 1827717763801546752 |
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| author | Tu T.N. Luong Jong-Myon Kim |
| author_facet | Tu T.N. Luong Jong-Myon Kim |
| author_sort | Tu T.N. Luong |
| collection | DOAJ |
| description | Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability. |
| first_indexed | 2024-03-10T20:09:01Z |
| format | Article |
| id | doaj.art-f49b7516d5734411b260c20fe0bfd1a2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:09:01Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f49b7516d5734411b260c20fe0bfd1a22023-11-19T23:04:47ZengMDPI AGSensors1424-82202020-04-01209254210.3390/s20092542The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training DataTu T.N. Luong0Jong-Myon Kim1Department of Computer Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of IT Convergence, University of Ulsan, Ulsan 44610, KoreaLeakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.https://www.mdpi.com/1424-8220/20/9/2542intelligent leak detectionacoustic emission signalsstatistical parameterssupport vector machinewavelet denoisingShannon entropy |
| spellingShingle | Tu T.N. Luong Jong-Myon Kim The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data Sensors intelligent leak detection acoustic emission signals statistical parameters support vector machine wavelet denoising Shannon entropy |
| title | The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data |
| title_full | The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data |
| title_fullStr | The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data |
| title_full_unstemmed | The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data |
| title_short | The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data |
| title_sort | enhancement of leak detection performance for water pipelines through the renovation of training data |
| topic | intelligent leak detection acoustic emission signals statistical parameters support vector machine wavelet denoising Shannon entropy |
| url | https://www.mdpi.com/1424-8220/20/9/2542 |
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