Monitoring Pipelines Through Acoustic Method.

Sousa, Elisangela Orlandi de; Cruz, Sandra Lúcia da; Pereira, João Alexandre Ferreira da Rocha

doi:10.1016/s1570-7946(09)70642-x

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Artificial neural network technique can be used for detection and identification of multiple leaks in a pipeline [31]. The dynamic of the fluid throughout the pipeline (see Figure 10) can be defined as following mathematical model, (1) such that is taken to be the pressure head, is taken to be the flow, is taken to be the length coordinate, is taken to be the time coordinate, is considered as the acceleration of the gravity, is considered as the crosssection area, is taken to be the speed of sound, and . is considered as the pipeline diameter, and is the Darcy-Weissbach friction coefficient.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

“…Identifying leaks in early stage is essential to prevent serious problems. In order to improve levels of security, dependability and usefulness of operation, novel approaches for pipeline observation have been generated worldwide [1]. Distributed temperature monitoring approaches utilizing optical fibers have been considered as an effective method to identify and localize leakages throughout pipelines [2].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Jafari

Razvarz

Gegov

et al. 2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

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Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced.

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Section: A Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Jafari

Razvarz

Gegov

et al. 2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

View full text Add to dashboard Cite

show abstract

“…Aiming at improving levels of safety, reliability, efficiency and effectiveness of operation, new technologies for pipeline monitoring have been supported and developed worldwide (Sousa et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

Santos

Sousa

Silva

et al. 2014

Braz. J. Chem. Eng.

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-Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.

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Crack detection and leakage monitoring on reinforced concrete pipe

Feng

Kong

Huo

et al. 2015

Smart Mater. Struct.

115

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Reinforced concrete underground pipelines are some of the most widely used types of structures in water transportation systems. Cracks and leakage are the leading causes of pipeline structural failures which directly results in economic losses and environmental hazards. In this paper, the authors propose a piezoceramic based active sensing approach to detect the cracks and the further leakage of concrete pipelines. Due to the piezoelectric properties, piezoceramic material can be utilized as both the actuator and the sensor in the active sensing approach. The piezoceramic patch, which is sandwiched between protective materials called 'smart aggregates,' can be safely embedded into concrete structures. Circumferential and axial cracks were investigated. A wavelet packet-based energy analysis was developed to distinguish the type of crack and determine the further leakage based on different stress wave energy attenuation propagated through the cracks.

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Monitoring Pipelines Through Acoustic Method.

Cited by 4 publications

References 4 publications

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

Crack detection and leakage monitoring on reinforced concrete pipe

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