Autonomous low-cost Wireless Sensor platform for Leakage Detection in Oil and Gas Pipes

Christos, Spandonidis C.; Giannopoulos, Fotis; Galiatsatos, Nektarios; Reppas, Dimitris; Petsa, Areti; Dimitrios, Spyropoulos

doi:10.1109/mocast52088.2021.9493340

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Although optical fiber cable can monitor changes in temperature in various locations, it fails both in monitoring small leakages and determining the exact location of a leakage [ 7 , 8 ]. Christos et al [ 9 , 10 ] proposed a low-energy and low-cost wireless sensor-based system to immediately detect leakage in metallic pipes. They monitor the changes in the effects of the vibrational signal appearing due to leakage on the pipeline walls.…”

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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Section: Introductionmentioning

confidence: 99%

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Ullah

Ahmad

Kim

2023

Sensors

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“…It has been successfully used for leak detection. In [17], a low-cost wireless sensor system it [12][13][14][15][16]. It has been successfully used for leak detection.…”

Section: Introductionmentioning

confidence: 99%

“…It has been successfully used for leak detection. In [17], a low-cost wireless sensor system is introduced to detect of leaks in metallic piping systems. In [18,19], a neural network technique was utilized to detect the leak in a pipeline and has provided promising results.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leak Detection and Estimation Using Fuzzy PID Observer

et al. 2022

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A pipe is a ubiquitous product in the industries that is used to convey liquids, gases, or solids suspended in a liquid, e.g., a slurry, from one location to another. Both internal and external cracking can result in structural failure of the industrial piping system and possibly decrease the service life of the equipment. The chaos and complexity associated with the uncertain behaviour inherent in pipeline systems lead to difficulty in detection and localisation of leaks in real time. The timely detection of leakage is important in order to reduce the loss rate and serious environmental consequences. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. In this work, the ARX–Laguerre model has been used to generate better performance in the presence of uncertainty. According to the results, the proposed technique can detect leaks accurately and effectively.

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Assessing Gas Leakage Detection Performance Using Machine Learning with Different Modalities

Kumar,

Singh,

Pandey

2024

Trans. Electr. Electron. Mater.

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Autonomous low-cost Wireless Sensor platform for Leakage Detection in Oil and Gas Pipes

Cited by 5 publications

References 6 publications

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Pipeline Leak Detection and Estimation Using Fuzzy PID Observer

Assessing Gas Leakage Detection Performance Using Machine Learning with Different Modalities

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