Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network
This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2023.2165159 |
| _version_ | 1797403916101484544 |
|---|---|
| author | Zahoor Ahmad Tuan-Khai Nguyen Jong-Myon Kim |
| author_facet | Zahoor Ahmad Tuan-Khai Nguyen Jong-Myon Kim |
| author_sort | Zahoor Ahmad |
| collection | DOAJ |
| description | This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions. |
| first_indexed | 2024-03-09T02:46:22Z |
| format | Article |
| id | doaj.art-8bd9c4f3ded54e32952479badb66b2b9 |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-03-09T02:46:22Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-8bd9c4f3ded54e32952479badb66b2b92023-12-05T16:53:44ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2023-12-0117110.1080/19942060.2023.2165159Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural networkZahoor Ahmad0Tuan-Khai Nguyen1Jong-Myon Kim2Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaPD Technology Co. Ltd., Ulsan,South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaThis paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions.https://www.tandfonline.com/doi/10.1080/19942060.2023.2165159Pipelinesacoustic emissionleak detection1-D convolutional neural network |
| spellingShingle | Zahoor Ahmad Tuan-Khai Nguyen Jong-Myon Kim Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network Engineering Applications of Computational Fluid Mechanics Pipelines acoustic emission leak detection 1-D convolutional neural network |
| title | Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network |
| title_full | Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network |
| title_fullStr | Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network |
| title_full_unstemmed | Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network |
| title_short | Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network |
| title_sort | leak detection and size identification in fluid pipelines using a novel vulnerability index and 1 d convolutional neural network |
| topic | Pipelines acoustic emission leak detection 1-D convolutional neural network |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2023.2165159 |
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