The development of leak detection model in subsea gas pipeline using machine learning

Kim, Ju-Hyun; Chae, Minju; Han, Joong‐Soo; Park, Simon; Lee, Youngsoo

doi:10.1016/j.jngse.2021.104134

Cited by 32 publications

(14 citation statements)

References 18 publications

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“…In recent years, several leak detection and location methods based on artificial intelligence algorithms have been proposed. This paper divides these technologies into two categories: (1) leak recognition or detection method based on machine learning algorithms [5][6][7][8] and (2) leak recognition or detection method based on deep learning algorithms [9][10][11][12]. These two methods collect leak acoustic signal, pressure signal, flow signal, or transient water hammer wave signal of the water pipes to build leak dataset.…”

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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“…ML is a subset of artificial intelligence, namely algorithms that improve through previous data records and experience [ 25 , 26 ]. The algorithms focus on building mathematical models using training data or sample data to make decisions or predictions without explicit programming [ 27 ]. When properly executed, machine learning can enable tasks to be automated at a breakneck pace.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Ullah

Ahmad

Kim

2023

Sensors

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Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.

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“…The World Energy Report 2020 indicates that using natural fluids and fossil fuels for energy production accounts for thirty percent of the world's demand [1]. The rapid growth of the world's population as well as the acceleration of industrialization, have led to a rise in the use of fossil fuels such as petroleum and natural gas [2].…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of a pipeline leak detection using smart inspection ball

2023

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Recently, pipelines have replaced more carbon-intensive transportation methods making them more environmentally friendly for transporting energy and water supplies. However, pipelines can pollute the air, water, soil, and climate when they leak, causing economic, and environmental damage. Pipeline online monitoring provides data analysis and suitable controlling strategies to contain the risk. This paper proposes a three-dimensional numerical model simulation taking advantage of the fluids moving through pipelines at specific speeds. The transport speeds depend on many conditions, such as pipe diameter, the pressure through which the fluid is being transported, and other factors, such as terrain's topography and viscosity of the fluid. Under these conditions, the inspection approach uses a self-charging movable ball. The sensors inside the ball capture data as it travels through the pipe. The simulation focuses on spherical flow and pipe noise with and without leakage based on the COMSOL software platform. The paper shows the effect of several parameters, including leak location, sensor placement, ball diameter, sound pressure level propagation along a pipe and around the sphere, velocity, and temperature distribution that give the background for future smart ball design in a promising practical pipeline test project.

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The development of leak detection model in subsea gas pipeline using machine learning

Cited by 32 publications

References 18 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

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Modeling and simulation of a pipeline leak detection using smart inspection ball

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