A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.

WAVELET TRANSFORM THRESHOLD NOISE REDUCTION METHODS IN THE OIL PIPELINE LEAKAGE MONITORING AND POSITIONING SYSTEM

This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.

Simulation Study on the Typical Influencing Factors for Negative Pressure Wave in Liquid Pipeline Leakage

We conduct simulation study on the typical influencing factors for negative pressure wave in liquid pipeline leakage. We first analyse the liquid pipeline leakage detection based on negative pressure wave method and obtain the essential simulation parameters. Then based on the physical model of pipeline and by introducing leakage boundary condition, we simulate the variation of pressure and flow rate in pipeline after leakage, the influence of leakage scale and leakage position on the pressure and flow rate in the pipeline. The results show that the leakage scale mainly influences the amplitude of negative pressure wave, and that the leakage position inflnenees both the amplitude and the shape of the curves of negative pressure wave.

Modeling of Hydrogen Blending on the Leakage and Diffusion of Urban Buried Hydrogen-Enriched Natural Gas Pipeline

With the introduction of various carbon reduction policies around the world,hydrogen energy,as a kind of clean energy with zero carbon emission,has attracted much attention.The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries.Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way.However,hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas.Therefore,it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas(HENG)pipelines for the safe transportation of hydrogen energy.In this study,the leakage and diffusion characteristics of urban buried HENG pipelines are investigated numerically,and the dangerous degree of leakage is analyzed based on the time and area when the gas concentration reaches the lower explosive limit.The influences of hydrogen blending ratio(HBR),operating pressure,leakage hole size and direction,as well as soil type on the leakage and diffusion law of HENG are analyzed.Results show that the hydrogen mixing is not the key factor in increasing the degree of risk after gas leakage for urban buried HENG pipelines.When the HBR is 5%,10%,15% and 20%,the corresponding first dangerous time is 1053,1041,1019 and 998 s,respectively.Thiswork is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future.

Development of systems for detection,early warning,and control of pipeline leakage in drinking water distribution:A case study

Water leakage in drinking water distribution systems is a serious problem for many cities and a huge challenge for water utilities.An integrated system for the detection,early warning,and control of pipeline leakage has been developed and successfully used to manage the pipeline networks in selected areas of Beijing.A method based on the geographic information system has been proposed to quickly and automatically optimize the layout of the instruments which detect leaks.Methods are also proposed to estimate the probability of each pipe segment leaking （on the basis of historic leakage data）,and to assist in locating the leakage points （based on leakage signals）.The district metering area （DMA） strategy is used.Guidelines and a flowchart for establishing a DMA to manage the large-scale looped networks in Beijing are proposed.These different functions have been implemented into a central software system to simplify the day-to-day use of the system.In 2007 the system detected 102 non-obvious leakages （i.e.,14.2% of the total detected in Beijing） in the selected areas,which was estimated to save a total volume of 2,385,000 m 3 of water.These results indicate the feasibility,efficiency and wider applicability of this system.

Convolutional Neural Network-based Leakage Detection of Crude Oil Transmission Pipes

Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspection method for detection.This traditional method is not only inefficient but also labor-intensive.The present paper proposes a novel convolutional neural network(CNN)architecture for automatic leakage level assessment of crude oil transmission pipes.An experimental setup is developed,where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions.Specifically,images are collected from various pipes with no leaking and different leaking states.Apart from images from existing pipelines,images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world.The main contributions of the present paper are,developing a convolutional neural network to classify the information in red-green-blue(RGB)and thermal images,development of the experimental setup,conducting leakage experiments,and analyzing the data using the developed approach.By successfully combining the two types of images,the proposed method is able to achieve a higher classification accuracy,compared to other methods that use RGB images or thermal images alone.Especially,compared with the method that uses thermal images only,the accuracy increases from about 91%to over 96%.

Characteristics of Vibrational Wave Propagation and Attenuation in Submarine Fluid-Filled Pipelines

As an important part of lifeline engineering in the development and utilization of marine resources, the submarine fluid-filled pipeline is a complex coupling system which is subjected to both internal and external flow fields. By utilizing Kennard＇s shell equations and combining with Helmholtz equations of flow field, the coupling equations of submarine fluid-filled pipeline for n=0 axisymmetrical wave motion are set up. Analytical expressions of wave speed are obtained for both s=1 and s=2 waves, which correspond to a fluid-dominated wave and an axial shell wave, respectively. The numerical results for wave speed and wave attenuation are obtained and discussed subsequently. It shows that the frequency depends on phase velocity, and the attenuation of this mode depends strongly on material parameters of the pipe and the internal and the external fluid fields. The characteristics of PVC pipe are studied for a comparison. The effects of shell thickness/radius ratio and density of the contained fluid on the model are also discussed. The study provides a theoretical basis and helps to accurately predict the situation of submarine pipelines, which also has practical application prospect in the field of pipeline leakage detection.