Leak diagnosis in pipelines using a combined artificial neural network approach

Pérez-Pérez, E.J.; López‐Estrada, Francisco‐Ronay; Valencia‐Palomo, Guillermo; Torres, Lizeth; Puig, Vicenç; Antonio, Jesús Darío Mina

doi:10.1016/j.conengprac.2020.104677

Cited by 52 publications

(36 citation statements)

References 55 publications

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“…This methodology demonstrated the potential of the combined use of both fluid transient pressure waves and ANNs to detect leak features in pipelines. Pérez [14] also proposed an improved ANNs leak diagnosis for fluid transport pipelines. In this methodology, the pressure and flow rate were acquired as original data for ANNs, and the pipe friction factor was used as an input to estimate the leak point.…”

Section: Related Workmentioning

confidence: 99%

Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Liu

Yang

Zheng

2021

Entropy

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Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.

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Section: Related Workmentioning

confidence: 99%

Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Liu

Yang

Zheng

2021

Entropy

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“…In recent years, machine learning methods have been increasingly used for leakage detection and localization. Zhou et al [19] and Pérez-Pérez et al [20] investigated leak detection in a single pipeline. In the Zhou et al [19]'s work, a convolutional neural network (CNN) was used to pinpoint leak locations in a 1500 m long pipe segment for different leak sizes, where the better prediction was obtained for greater leakages.…”

Section: Introductionmentioning

confidence: 99%

“…In the Zhou et al [19]'s work, a convolutional neural network (CNN) was used to pinpoint leak locations in a 1500 m long pipe segment for different leak sizes, where the better prediction was obtained for greater leakages. Pérez-Pérez et al [20] used a combined artificial neural network (ANN), where the ANN is first used to estimate the friction factor of the pipe and then to localize leak location. Tests were conducted for a 64.48 m pipe, for which it was reported that an average percentage error of 0.47% was achieved.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

Lučin

Čarija

et al. 2021

Mathematics

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In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of ±2.5%). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation ±20% the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of ±10% the accuracy increased from 36% to 77%).

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“…Prediction of failure of water mains was investigated in [6] where artificial neural network (ANN), ridge regression, and ensemble decision tree were used. Different machine learning algorithms have been explored for the prediction of leak locations in pipelines, such as convolutional neural network (CNN) [7], [8] and ANN [9], [10]. In [11] support vector machine (SVM) method was used to predict leaks in wall-mounted pipelines.…”

Section: Introductionmentioning

confidence: 99%

Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

et al. 2021

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Leak diagnosis in pipelines using a combined artificial neural network approach

Cited by 52 publications

References 55 publications

Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

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