A Novel Custom One-Dimensional Time-Series DenseNet for Water Pipeline Leak Detection and Localization Using Acousto-Optic Sensor

A crucial component within any structural health monitoring system is a pipeline leak detection mechanism, vital for preventing avoidable water loss. Contemporary literature employs machine learning and deep learning for detecting pipeline leaks and cross-correlation for leak localization. The major...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.7966-7973
Main Authors: Rajasekaran, Uma, Kothandaraman, Mohanaprasad
Format: Article
Language:English
Subjects:
Acousto-optic sensor
Acousto-optics
and pipeline leak detection and localization
Artificial neural networks
CNN
Cross correlation
Deep learning
DenseNet
Leak detection
Localization
Machine learning
Noise reduction
Sensors
Structural health monitoring
Time series
Water loss
Water pipelines
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Summary:A crucial component within any structural health monitoring system is a pipeline leak detection mechanism, vital for preventing avoidable water loss. Contemporary literature employs machine learning and deep learning for detecting pipeline leaks and cross-correlation for leak localization. The major drawbacks in the existing methodologies are that machine learning and deep learning methods need two different architectures for leak detection and localization, and the cross-correlation needs two sensors with a denoising technique. The primary objective of this paper is to deploy a unified architecture capable of executing both the detection and localization of a leak without any denoising technique and with a single sensor. The proposed technique utilizes the data collected using an Acousto-optic sensor with two different pressures. This paper proposes a novel custom one-dimensional time-series DenseNet for leak detection and localization. The proposed method gives better accuracies compared with the existing one-dimensional DenseNet-121, three different one-dimensional convolutional neural networks (1DCNN), and cross-correlation for two different pressure datasets. The proposed method’s processing time is thirteen times less than the existing one-dimensional DenseNet-121, with the observed average leak detection and localization accuracy of 99.08%. The results state that the proposed novel custom one-dimensional time-series DenseNet accurately detects and localizes the leak with less time.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3352646