Water leak detection based on convolutional neural network using actual leak sounds and the hold-out method
The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data. In coll...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c2ca1ab646d24faab4a5f480ce3a704a |
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| Sumario: | The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data. In collaboration with a pipeline restoration company, 20 acoustic datasets of leak sounds were recorded by sensors at 10 leak sites. The detection ability of the constructed CNN model was tested using the hold-out method for the 20 cases: 19 showed more than 70% accuracy, of which 15 showed more than 80%. HIGHLIGHTS
We are introducing a next-generation leak detection technique.;
We are targeting the analysis of actual leaks, not virtual.;
We visualised the inherent characteristics of water leak sound.;
This study introduces leak detection techniques through artificial intelligence technology.;
The leak detection model proposed in this study has been proven to have sufficient reliability.; |
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