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Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines
The occurrence of bursts in water pipelines can not only prevent the system from functioning properly, but it can also produce significant water loss that disrupts activities in urban areas. Therefore, the detection and location of bursts in water distribution systems is a vital task for water utilities. Various techniques currently exist to detect the occurrence of these events, but there is a need for a permanent monitoring method that can detect and identify anomalous events quickly and accurately. This paper presents a new technique that uses artificial neural networks (ANNs) to detect and identify bursts in pipelines by interpreting the transient pressure waves that a burst causes along pipelines. The technique is divided into two stages: a model development stage and an application stage. The model development stage includes the generation of transient pressure traces and the training and testing of two different ANNs to (1) detect burst occurrence and (2) identify burst location and size. The application stage includes the processing of a potentially continuous transient pressure trace, analysis by the previously trained ANNs, and then the verification of the results using a transient flow forward numerical model. A numerical application demonstrates the principles of the technique and the potential for merging the use of fluid transient waves and ANNs. The technique has also been validated in the laboratory, indicating that the prediction of the location of the burst is very accurate while the prediction of the burst size requires an additional step to ensure its accuracy.
Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines
The occurrence of bursts in water pipelines can not only prevent the system from functioning properly, but it can also produce significant water loss that disrupts activities in urban areas. Therefore, the detection and location of bursts in water distribution systems is a vital task for water utilities. Various techniques currently exist to detect the occurrence of these events, but there is a need for a permanent monitoring method that can detect and identify anomalous events quickly and accurately. This paper presents a new technique that uses artificial neural networks (ANNs) to detect and identify bursts in pipelines by interpreting the transient pressure waves that a burst causes along pipelines. The technique is divided into two stages: a model development stage and an application stage. The model development stage includes the generation of transient pressure traces and the training and testing of two different ANNs to (1) detect burst occurrence and (2) identify burst location and size. The application stage includes the processing of a potentially continuous transient pressure trace, analysis by the previously trained ANNs, and then the verification of the results using a transient flow forward numerical model. A numerical application demonstrates the principles of the technique and the potential for merging the use of fluid transient waves and ANNs. The technique has also been validated in the laboratory, indicating that the prediction of the location of the burst is very accurate while the prediction of the burst size requires an additional step to ensure its accuracy.
Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines
Bohorquez, Jessica (author) / Simpson, Angus R. (author) / Lambert, Martin F. (author) / Alexander, Bradley (author)
2020-11-09
Article (Journal)
Electronic Resource
Unknown
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