Water Pipeline Leak Detection and Localization With an Integrated AI Technique
A pipeline leak detection and localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. The main aim of this approach is to propose a standalone architecture for leak detection and localization using a single sensor. The sensor used in thi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819371/ |
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| author | Uma Rajasekaran Mohanaprasad Kothandaraman Chang Hong Pua |
| author_facet | Uma Rajasekaran Mohanaprasad Kothandaraman Chang Hong Pua |
| author_sort | Uma Rajasekaran |
| collection | DOAJ |
| description | A pipeline leak detection and localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. The main aim of this approach is to propose a standalone architecture for leak detection and localization using a single sensor. The sensor used in this approach is an Acousto-optic vibration sensor, which is highly sensitive to capture the vibrations caused by the pipeline leak. The proposed standalone architecture contains two steps: 1) Feature extraction and 2) leak detection and localization. This approach uses a one-dimensional convolutional neural network (1DCNN) for feature extraction. This paper tunes the AdaBoost to have support vector machines (SVM), Decision Trees (DT), and multi-layer perceptron (MLP) instead of the inbuilt weak estimators to give improved performance. The modified AdaBoost detects and localizes the leak by classifying the leak locations. The proposed 1DCNN-modified AdaBoost’s performance is cross-verified with nine models and cross-correlation. All the models are tested with 200000 and 300000 Pascal pressure to check the stability. The proposed 1DCNN-modified AdaBoost outperforms all the other methods implemented in this research. In the future, this research can be extended with different leak sizes and pipeline materials and real-time pipeline environments with longer distances. |
| format | Article |
| id | doaj-art-61217eb777c3400185213836a6944663 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-61217eb777c3400185213836a69446632025-01-07T00:02:23ZengIEEEIEEE Access2169-35362025-01-01132736274510.1109/ACCESS.2024.352466910819371Water Pipeline Leak Detection and Localization With an Integrated AI TechniqueUma Rajasekaran0https://orcid.org/0000-0002-3344-9854Mohanaprasad Kothandaraman1https://orcid.org/0000-0003-3938-7495Chang Hong Pua2https://orcid.org/0000-0001-7382-6450School of Electronics Engineering (SENSE), VIT University, Chennai, Tamil Nadu, IndiaSchool of Electronics Engineering (SENSE), VIT University, Chennai, Tamil Nadu, IndiaDepartment of Electrical and Electronics, LKC Faculty of Engineering and Science, Universiti Tunku Abdul Rahman (UTAR), Sungai Long Campus, Kampar, MalaysiaA pipeline leak detection and localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. The main aim of this approach is to propose a standalone architecture for leak detection and localization using a single sensor. The sensor used in this approach is an Acousto-optic vibration sensor, which is highly sensitive to capture the vibrations caused by the pipeline leak. The proposed standalone architecture contains two steps: 1) Feature extraction and 2) leak detection and localization. This approach uses a one-dimensional convolutional neural network (1DCNN) for feature extraction. This paper tunes the AdaBoost to have support vector machines (SVM), Decision Trees (DT), and multi-layer perceptron (MLP) instead of the inbuilt weak estimators to give improved performance. The modified AdaBoost detects and localizes the leak by classifying the leak locations. The proposed 1DCNN-modified AdaBoost’s performance is cross-verified with nine models and cross-correlation. All the models are tested with 200000 and 300000 Pascal pressure to check the stability. The proposed 1DCNN-modified AdaBoost outperforms all the other methods implemented in this research. In the future, this research can be extended with different leak sizes and pipeline materials and real-time pipeline environments with longer distances.https://ieeexplore.ieee.org/document/10819371/1DCNNacousto-optic vibration sensorDTmodified AdaBoostpipeline leak detection and localizationSVM |
| spellingShingle | Uma Rajasekaran Mohanaprasad Kothandaraman Chang Hong Pua Water Pipeline Leak Detection and Localization With an Integrated AI Technique IEEE Access 1DCNN acousto-optic vibration sensor DT modified AdaBoost pipeline leak detection and localization SVM |
| title | Water Pipeline Leak Detection and Localization With an Integrated AI Technique |
| title_full | Water Pipeline Leak Detection and Localization With an Integrated AI Technique |
| title_fullStr | Water Pipeline Leak Detection and Localization With an Integrated AI Technique |
| title_full_unstemmed | Water Pipeline Leak Detection and Localization With an Integrated AI Technique |
| title_short | Water Pipeline Leak Detection and Localization With an Integrated AI Technique |
| title_sort | water pipeline leak detection and localization with an integrated ai technique |
| topic | 1DCNN acousto-optic vibration sensor DT modified AdaBoost pipeline leak detection and localization SVM |
| url | https://ieeexplore.ieee.org/document/10819371/ |
| work_keys_str_mv | AT umarajasekaran waterpipelineleakdetectionandlocalizationwithanintegratedaitechnique AT mohanaprasadkothandaraman waterpipelineleakdetectionandlocalizationwithanintegratedaitechnique AT changhongpua waterpipelineleakdetectionandlocalizationwithanintegratedaitechnique |