A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the pipeline ages and corrodes, it becomes susceptible to deterioration, resulting in wastage and hazardous damages depending on the material it transports. To mitigate these risks, implementing a suitable monitoring...

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Bibliographic Details
Published in:Journal of pipeline systems 2024-05, Vol.15 (2)
Main Authors: Rajasekaran, Uma, Kothandaraman, Mohanaprasad
Format: Article
Language:English
Subjects:
Accuracy
Acousto-optics
Artificial intelligence
Artificial neural networks
Biological computing
Cross correlation
Damage detection
Detection
Feature extraction
Independent component analysis
Leak detection
Localization
Monitoring
Monitoring systems
Petrochemicals
Pipelines
Signal processing
Slurries
Slurry pipelines
Surveys
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Summary:A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the pipeline ages and corrodes, it becomes susceptible to deterioration, resulting in wastage and hazardous damages depending on the material it transports. To mitigate these risks, implementing a suitable monitoring system becomes essential, enabling the early identification of damage and minimizing waste and the potential for hazardous incidents. The pipeline monitoring system can be exterior, visual/biological, and computational. This paper surveys state-of-the-art approaches and also performs experimental analyses with a few methods in signal/data-driven approaches within computational methods. More precisely, signal processing-based leak localization methods, artificial intelligence-based leak detection methods, and combined approaches are given. This paper implements five signal processing-based methods and 17 artificial intelligence-based methods. This implementation helps to compare and understand the significance of appropriate noise removal and feature extraction. The data for this analysis is collected using acousto-optic sensors from an experimental setup. After implementation, the highest observed leak localization accuracy is 99.14% with the wavelet packet adaptive independent component analysis-based generalized cross correlation, and the highest leak detection accuracy is 98.32% with the one-dimensional convolutional neural network.
ISSN:1949-1190
1949-1204
DOI:10.1061/JPSEA2.PSENG-1611