Multimodel Neural Network for Live Classification of Water Pipe Leaks from Vibro-Acoustic Signals

Gunatilake, A; Miro, JV

Multimodel Neural Network for Live Classification of Water Pipe Leaks from Vibro-Acoustic Signals

Gunatilake, A Miro, JV

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2024, 24, (9), pp. 14825-14832
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Gunatilake, A
dc.contributor.author	Miro, JV
dc.date.accessioned	2024-08-07T05:11:31Z
dc.date.available	2024-08-07T05:11:31Z
dc.date.issued	2024-05-01
dc.identifier.citation	IEEE Sensors Journal, 2024, 24, (9), pp. 14825-14832
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/180280
dc.description.abstract	A novel neural network model able to identify the nature of leaks in a live urban underground water pipe grid is proposed in this article. Traditional leak detection methods are often limited to detecting the existence or otherwise of a possible leak in the signals, yet do not provide information about the actual nature of the leak. This is of utmost importance for utilities to be able to rank and schedule their repair and rehabilitation efforts. The proposed model, termed multiclass time-frequency convolutional neural network (M-TFCNN), uses a multimodel categorization approach, enabling the identification of various types of leak-related events, namely, pipe breaks, hydrant leaks, service valve leaks, private leaks, and others. The proposed neural network architecture consists of a feature extraction module, a convolutional base, and a classification head, which has been meticulously engineered through numerous experiments and iterations. The model was trained on a large-scale dataset of vibroacoustic signals collected from an operating water main network in a major Australian city. The data encompass a substantive signal collection of validated representations of different types of leaks gathered over a span of three and a half years. The model's performance is evaluated using traditional metrics such as accuracy, precision, recall, and F1-score. The presented results show that the proposed model surpasses conventional leak detection methods, accurately identifying different types of water leaks and achieving accuracies of up to 98%. Overall, the proposed neural network model represents a notable practical step forward in the field of water leak detection by subcategorizing leaks and has the potential to revolutionize the way industry practitioners manage larger water infrastructure.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2024.3379476
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	40 Engineering
dc.title	Multimodel Neural Network for Live Classification of Water Pipe Leaks from Vibro-Acoustic Signals
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:11:15Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	9

Abstract:

A novel neural network model able to identify the nature of leaks in a live urban underground water pipe grid is proposed in this article. Traditional leak detection methods are often limited to detecting the existence or otherwise of a possible leak in the signals, yet do not provide information about the actual nature of the leak. This is of utmost importance for utilities to be able to rank and schedule their repair and rehabilitation efforts. The proposed model, termed multiclass time-frequency convolutional neural network (M-TFCNN), uses a multimodel categorization approach, enabling the identification of various types of leak-related events, namely, pipe breaks, hydrant leaks, service valve leaks, private leaks, and others. The proposed neural network architecture consists of a feature extraction module, a convolutional base, and a classification head, which has been meticulously engineered through numerous experiments and iterations. The model was trained on a large-scale dataset of vibroacoustic signals collected from an operating water main network in a major Australian city. The data encompass a substantive signal collection of validated representations of different types of leaks gathered over a span of three and a half years. The model's performance is evaluated using traditional metrics such as accuracy, precision, recall, and F1-score. The presented results show that the proposed model surpasses conventional leak detection methods, accurately identifying different types of water leaks and achieving accuracies of up to 98%. Overall, the proposed neural network model represents a notable practical step forward in the field of water leak detection by subcategorizing leaks and has the potential to revolutionize the way industry practitioners manage larger water infrastructure.

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http://hdl.handle.net/10453/180280