Water leak detection based on convolutional neural network using actual leak sounds and the hold-out method
| Autor: | Y. W. Nam, Y. Arai, T. Kunizane, A. Koizumi |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Water Supply, Vol 21, Iss 7, Pp 3477-3485 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1606-9749 1607-0798 |
| DOI: | 10.2166/ws.2021.109 |
| Popis: | 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.; |
| Databáze: | Directory of Open Access Journals |
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