An integrated detection and location model for leakages in liquid pipelines

Liu, Cuiwei; Li, Yuxing; Xu, Minghai

doi:10.1016/j.petrol.2018.12.078

Cited by 59 publications

(21 citation statements)

References 29 publications

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“…In recent existing studies, there is a focus on small leakage detection. In comparison with a large leakage area, small leakages do not make significant changes in the pressure and flow of the liquid and, therefore, it is difficult to detect them [ 9 , 10 , 11 ]. In liquid leakage detection, a clear definition of small drops is challenging and existing literature measures the size of leaking drops differently.…”

Section: Requirements For Efficient Leakage Detectionmentioning

confidence: 99%

Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Fahimipirehgalin

Vogel‐Heuser

Trunzer

et al. 2020

Sensors

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Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

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Section: Requirements For Efficient Leakage Detectionmentioning

confidence: 99%

Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Fahimipirehgalin

Vogel‐Heuser

Trunzer

et al. 2020

Sensors

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“…Para el desarrollo de la investigación se creó una interfaz de usuario, que le proporcionan al operador datos de caudal y presión, gráficas y controles para la correcta visualización y automatización del sistema de detección de fugas la Figura 5 muestra la interfaz. Caudal entrada: 1 = 0,0005782 3 (17) Caudal salida: 2 = 0,0005028 3 (18) Presión p_rgh: 1 = 13306,313 (19) También es importante recalcar como se obtuvieron algunos valores para poder ejecutar toda la operación como se muestran en las ecuaciones (20), (21)…”

Section: B Recopilación De Datosunclassified

“…En el diagnóstico de fugas, no solo es importante detectar la fuga y cuantificarla, sino que es una prioridad localizarla con la mayor precisión posible [19]. Por lo tanto, es necesario tener algoritmos que determinen con precisión la ubicación de la fuga porque no siempre es visible desde fuera de la tubería [20] y [21].…”

Section: Introductionunclassified

Modelo numérico de detección de fugas para sistema de tuberias.

Camperos

García-Guarín

Nolasco-Serna

2020

AiBi Revista de Investigación, Administración e Ingeniería

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La distribución usando redes hidráulicas define la planeación, producción y flexibilidad de la hidro-gestión en empresas. Sin embargo, las fugas representan perdidas económicas en términos de mantenimiento y ubicación de las fallas. En este contexto, el flujo y presión al momento de que ocurra un fallo son las variables de análisis. Como primera contribución de esta investigación proponemos simular las perdidas volumétricas de agua. De hecho, el software libre Openfoam es la herramienta computacional y el modelo de turbulencia k-ω se seleccionó para un fluido en transición, es decir fluido que esta entre el régimen laminar y turbulento. La segunda contribución de esta investigación consiste en un algoritmo para detectar fugas en un sistema de tuberías. El algoritmo está basado en Teorema de Reynolds. Respecto a los resultados obtenidos con el Teorema de Reynolds, podemos decir que resultados son comparados con una red hidráulica real, obteniendo errores porcentuales de 4% para los mejores datos y 9% para el peor, y una distancia promedio para encontrar la fuga igual a 2,7 metros. Se demostró que la presión cae de forma lineal de acuerdo con la ley de Darcy. En conexión con los resultados, en métodos tradicionales se instalan manómetros a lo largo de las tuberías para identificar esta caída de presión, si el número de manómetros no es suficiente, la fuga puede pasar indetectada. El método propuesto en este proyecto permite localizar la fuga únicamente utilizando 2 manómetros. Lo cual lo hace más práctico para el caso de tuberías de difícil acceso.

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“…Song and Li [6] proposed acoustic emission and artificial neural networks to detect leakage. In general, there are two categories of methods for detecting pipeline leakage, namely hardwarebased and software-based, or externally-based and internallybased [3], [7]. In hardware-based approaches, special sensing devices are used to detect pipeline leakage.…”

Section: Introductionmentioning

confidence: 99%

Leak Detection and Location Based on ISLMD and CNN in a Pipeline

et al. 2019

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The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized crosscorrelation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location. INDEX TERMS Local mean decomposition, convolutional neural network, generalized cross-correlation, leak detection and location, fault detection.

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An integrated detection and location model for leakages in liquid pipelines

Cited by 59 publications

References 29 publications

Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Modelo numérico de detección de fugas para sistema de tuberias.

Leak Detection and Location Based on ISLMD and CNN in a Pipeline

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