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.

Automatic Visual Leakage Detection and Localization from Pipelines in Chemical Process Plants Using Machine Vision Techniques

Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

Gas Leakage Detection and Pressure Difference Identification by Asymmetric Differential Pressure Method

Currently,the measurement methods for pneumatic system leakage include bubbling,ultrasonic,and pressure detection methods.These methods are sensitive to high-precision sensors,long detection times,and stable external environments.The traditional differential pressure method involves severe differential pressure fluctuations caused by environmental pressure fluctuations or electromagnetic noise interference of sensors,leading to inaccurate detection.In this paper,a differential pressure fitting method for an asymmetric differential pressure cylinder is proposed.It overcomes the limitation of the detection efficiency caused by the asynchronous temperature recovery of the two chambers in the asymmetric differential pressure method and uses the differential pressure substitution equation to replace the differential calculation of the differential pressure.The improved differential pressure method proposes an innovation based on the detection principle and calculation method.Additionally,the influence of the parameters in the differential pressure substitution equation on the leakage calculation results was simulated,and the specific physical significance of the parameters of the differential pressure substitution equation was explained.The experiments verified the fitting effect and proved the accuracy of this method.Compared with the traditional differential pressure method,the maximum leakage deviation of inhibition was 0.5 L/min.Therefore,this method can be used to detect leaks in air tanks.

Deep learning based water leakage detection for shield tunnel lining

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous accidents.To avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper.Our proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection head.It can extract representative features of leaking areas to aid inspection processes.To further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples.Validation experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

A Review on Distribution Model for Mobile Agent-Based Information Leakage Prevention

With the continuous use of cloud and distributed computing, the threats associated with data and information technology (IT) in such an environment have also increased. Thus, data security and data leakage prevention have become important in a distributed environment. In this aspect, mobile agent-based systems are one of the latest mechanisms to identify and prevent the intrusion and leakage of the data across the network. Thus, to tackle one or more of the several challenges on Mobile Agent-Based Information Leakage Prevention, this paper aim at providing a comprehensive, detailed, and systematic study of the Distribution Model for Mobile Agent-Based Information Leakage Prevention. This paper involves the review of papers selected from the journals which are published in 2009 and 2019. The critical review is presented for the distributed mobile agent-based intrusion detection systems in terms of their design analysis, techniques, and shortcomings. Initially, eighty-five papers were identified, but a paper selection process reduced the number of papers to thirteen important reviews.

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.

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.

HIRFL cooling water-monitoring system design and construct

Background HIRFL is a heavy ion accelerator built by the Institute of Modern Physics,Chinese Academy of Sciences,which constitutes a total length of over 900 m by the SFC,SSC,CSRm,CSRe and so on.It bears a lot of research task of the country,so to ensure its safe operation is very important.Purpose Cool-water system is a key part in HIRFL,and the parameters of the cool-water system need to be monitored before an accident occurs.Methods This article describes three aspects of the system,from the water leakage detection,pressure monitoring,to the water-level monitoring.A complete monitor system was designed and constructed,including the hardware structure;the data transfer mode,as well as specific control program.C++,FLASH and SCADA are used in the monitoring system.Results Fault alarm was performed timely and accurately by the monitoring system,and many accidents were avoided.Conclusion Water system monitoring is an important part of the accelerator control system.It will also play a major role in the construction of large-scale scientific device named HIAF in the further.