Review and analysis of pipeline leak detection methods

Korlapati, Naga Venkata Saidileep; Khan, Faisal Nadeem; Noor, Quddus; Mirza, Saadat; Vaddiraju, Sreeram

doi:10.1016/j.jpse.2022.100074

Cited by 69 publications

(33 citation statements)

References 42 publications

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“…Several studies have been performed on the leakage detection over various conditions [4][5][6][7][8]. Lang et al [9] proposed an improved local mean decomposition signal analysis for reducing the noise interference, and used the least squares twin support vector machine to recognize pipeline leaks.…”

Section: Introductionmentioning

confidence: 99%

Leakage detection based on variational mode decomposition and long short-term memory neural network

Zheng,

Yan,

et al. 2024

J. Phys.: Conf. Ser.

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In the process of long-term continuous operation, fluid transportation pipelines are prone to leakage accidents. Therefore, this study investigates the detection of small-sized leaks with a leakage aperture of 13 mm in pipes with a diameter of 100 mm. The experimental investigation is conducted under the following operating conditions: volume flow of 25-80 m3/h, pressure of 100-200 kPa. The variations in volume flow and pressure signals during leak occurrences are analysed. To mitigate the interference caused by noise, the variational mode decomposition (VMD) method is introduced. The VMD effectively reduces noise interference in the signals. Furthermore, the denoised signals are utilized to establish a long short-term memory neural network (LSTM). The LSTM model achieves a high accuracy rate of 91.67% for the entire dataset.

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Section: Introductionmentioning

confidence: 99%

Leakage detection based on variational mode decomposition and long short-term memory neural network

Zheng,

Yan,

et al. 2024

J. Phys.: Conf. Ser.

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“…Wang et al [ 4 ] discussed the ever-increasing energy needed to support oil distribution, pipeline distribution topological complexity, and the real-time assessment of distribution network safety. Korlapati et al [ 5 ] surveyed and classified leakage detection into three broad categories: visual inspections, internally determined/computational, and externally based methods and techniques. Fiber optic cable-based leakage detection in pipelines is carried out by laying down the cable alongside the pipes, and changes in strain and temperature are observed.…”

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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“…Pipeline transportation is one of the most important methods of transportation for oil and gas products [1,2]. It has the advantages of strong transportation capacity and low operating costs [3]. By the end of 2017, the total mileage of oil and gas pipelines in service worldwide reached 192 × 10 4 km.…”

Section: Introductionmentioning

confidence: 99%

A novel non-contact, magnetic-based stress inspection technology and its application to stress concentration zone diagnosis in pipelines

Liao

Leng

et al. 2023

Meas. Sci. Technol.

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During the construction and long-term operation, stress concentration zones (SCZs) will inevitably occur in buried pipelines. It may cause pipe leakage or fracture failure. Therefore, it is essential to inspect the precise position and stress damage degree of SCZs. Pipeline non-contact magnetic-based stress inspection (PMSI) is an important issue in the oil and gas pipeline stress inspection field. In order to determine the response of the non-contact magnetic gradient signal on stress damage of SCZs, we established a pipeline magnetic-based stress detection model to analyse the three-dimensional magnetic signal of SCZs. The effect of changes to the axial stress value, the stress concentration length, the detection height, and the gradiometer jitter is also studied. Moreover, the PMSI is carried out on a 10.3 km LNG buried pipeline, which is validated by the excavation inspection results. The results indicate that the Gx shows sinusoidal fluctuation near the SCZ. The Gy and Gz reach a peak value. The Gmax increases with the severity and length of SCZs. Two level-II SCZs and two level-III SCZs are found.

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Review and analysis of pipeline leak detection methods

Cited by 69 publications

References 42 publications

Leakage detection based on variational mode decomposition and long short-term memory neural network

Leakage detection based on variational mode decomposition and long short-term memory neural network

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

A novel non-contact, magnetic-based stress inspection technology and its application to stress concentration zone diagnosis in pipelines

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