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.

Detecting the backfill pipeline blockage and leakage through an LSTM-based deep learning model

Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.

Automatic Sensing and Detection for Subway Tunnel Pathologies

Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.

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.

A Hardware Trojan Detection Method Based on the Electromagnetic Leakage

Hardware Trojan(HT) refers to a special module intentionally implanted into a chip or an electronic system. The module can be exploited by the attacker to achieve destructive functions. Unfortunately the HT is difficult to detecte due to its minimal resource occupation. In order to achieve an accurate detection with high efficiency, a HT detection method based on the electromagnetic leakage of the chip is proposed in this paper. At first, the dimensionality reduction and the feature extraction of the electromagnetic leakage signals in each group(template chip, Trojan-free chip and target chip) were realized by principal component analysis(PCA). Then, the Mahalanobis distances between the template group and the other groups were calculated. Finally, the differences between the Mahalanobis distances and the threshold were compared to determine whether the HT had been implanted into the target chip. In addition, the concept of the HT Detection Quality(HTDQ) was proposed to analyze and compare the performance of different detection methods. Our experiment results indicate that the accuracy of this detection method is 91.93%, and the time consumption is 0.042s in average, which shows a high HTDQ compared with three other methods.

Online Magnetic Flux Leakage Detection System for Sucker Rod Defects Based on LabVIEW Programming

Aiming at the detection of the sucker rod defects,a real-time detection system is designed using the non-destructive testing technology of magnetic flux leakage(MFL).An MFL measurement system consists of many parts,and this study focuses on the signal acquisition and processing system.First of all,this paper introduces the hardware part of the acquisition system in detail,including the selection of the Hall-effect sensor,the design of the signal conditioning circuit,and the working process of the single chip computer(SCM)control serial port.Based on LabVIEW,a graphical programming software,the software part of the acquisition system is written,including serial port parameter configuration,detection signal recognition,original signal filtering,real-time display,data storage and playback.Finally,an experimental platform for the MFL detection is set up,and the MFL measurement is carried out on the transverse and longitudinal defects of the sucker rod surface.The experimental result shows that the designed acquisition and processing system has good detection performance,simple design and high flexibility.

Bubble counter based on photoelectric technique for leakage detection of cryogenic valves

In order to overcome the inconvenience of manual bubble counting, a bubble counter based on photoelectric technique aiming for automatically detecting and measuring minute gas leakage of cryogenic valves is proposed. Experiments have been conducted on a self-built apparatus, testing the performance with different gas inlet strategies (bottom gas-inlet strategy and side gas-inlet strategy) and the influence of gas pipe length (0, 1, 2, 4, 6, 8, 10 m) and leakage rate (around 10, 20, 30, 40 bubbles/min) on first bubble time and bubble rate. A buffer of 110 cm3 is inserted between leakage source and gas pipe to simulate the down- stream cavum adjacent to the valve clack. Based on analyzing the experimental data, experiential parameters have also been summarized to guide leakage detection and measurement for engineering applications. A practical system has already been suc- cessfully applied in a cryogenic testing apparatus for cryogenic valves.

Empirical Evaluation of Information Leakage Detection Using Net-flow Analysis

Because of the widespread of Trojans,organizations and Internet users become more vulnerable to the threat of information leakage.This paper describes an information leakage detection system( ILDS) to detect sensitive information leakage caused by Trojan.In particular,the principles of the system are based on the analysis of net-flows in four perspectives: heartbeat behavior analysis,DNS abnormal analysis,uploaddownload ratio and content analysis.Heartbeat behavior analysis and DNS abnormal analysis are used to detect the existence of Trojans while upload-download ratio and content analysis can quickly detect when the information leakage happens.Experiments indicate that the system is reliable and efficient in detecting information leakage.The system can also help to collect and preserve digital evidence when information leakage incident occurs.

Enhanced Side-Channel Leakage Detection Method by Considering Combinational Logic

In this paper,we propose a hybrid power model that includes the power consumption of not only the registers but also part of the combinational logic.By doing knownkey analysis with this hybrid model,power side-channel leakage caused by correct keys can be detected.In experiment,PRINTcipher and DES algorithms were chosen as analysis targets and combinational logic s-box unit was selected to build power template.The analysis results showed the signal-to-noise ratio(SNR) power consumption increase of more than 20%after considering s-box's power consumption so that the information of keys can be obtained with just half number of power traces.In addition,the side channel-leakage detection capability of our method also shows better effectiveness that can identify the correct keys.

Using acoustic technique to detect leakage in city gas pipelines

For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic theory of leakage detection and location was introduced. Then the physical relationship between instantaneous energy and structural state variation of a system was analyzed theoretically. With HILBERT-HUANG transformation (HHT), the instantaneous energy distribution feature of an unstable acoustic signal was obtained. According to the relative contribution method of the instantaneous energy, the noise in signal was eliminated effectively. Furthermore, in order to judge the leakage, the typical characteristic of the instantaneous energy of signal in the input and output end was discussed using correlative analysis. A number of experiments were carried out to classify the leakage from normal operations, and the results show that the leakages are successfully detected and the average recognition rate reaches 93.3% among three group samples. It is shown that the method using acoustic technique with IED and correlative analysis is effective and it may be referred in other pipelines.

Key technique of a detection sensor for coal mine wire ropes

Wire ropes,employed extensively in coal mine hoists and transportation systems are subject to damage due to wear,corrosion and fatigue.The extent of damage and the carrying capacity of ropes are closely related to the sense of safety by staff and equipments.Magnetic flux leakage detection method(MFL),as an effective method,is these days widely used in detection of broken strands of wire ropes.In order to improve the accuracy of detection of flaws in wire ropes by magnetic flux leakage(MFL),the effect of the distance between a sensor and the surface of a wire rope(i.e.,lift-off) on detection by magnetic flux leakage was in-vestigated.An analysis of the main principles for the choice of lift-off is described by us and a new method that improves the structure of the detector is proposed from the point of view of the design of a magnetic circuit,to restrain the impact of fluctuations of sensor lift-off.The effect of this kind of method is validated by simulation and computation.The results show that the detection sensitivity is markedly increased by this method.Furthermore,the signal-to-noise ratio(SNR) can be increased by over 28%.This method will lend itself to offer reliable scientific information to optimize the structure of excitation devices and improve the accuracy of MFL detection.

Instrument for insulation conditions detection of roof insulator on high-speed-train

With the rapid development of high-speed-railway,environment around high voltage device on train roof becomes very complicated. Most train accidents happened due to occurrence of flashover on roof insulator,but the insulation condition estimation of insulator in such environment is much difficult. To ensure the insulation property of electric equipment,and guarantee the operation safety of high-speed-train,here established an instrument with high reliability which can on-line monitor insulation condition of roof insulator and give out advanced alarm before the incipient insulator flashover. The instrument consists of three parts,Data Acquisition & Sensor,Data Processing and Back Processing. Anti-interference and protection methods are processed to Rogowski coil sensor for better leakage current signal. To avoid the fluctuation from railway power supply,four modules are set to filter the power supply waveform. Through laboratory measurement,it is shown that the leakage current and the impedance angle can be detected by the instrument accurately. From the comparison of leakage current and impedance angle results under different moisture condition and the alarm operation when leakage current value reached threshold,this instrument can give out enough information for staff to understand the insulation condition of insulator.

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.

Energy Saving Technology for Lowering Air Leakage of Sintering Pallets and Dust Collectors in Sinter Plant

The hot-wire type anemometers were used for measuring the velocity of effective air flowing through sinter bed in this study.Meanwhile,microphones were installed beside the pathway and close to the outer sidewall of travelling pallets for monitoring sound pressure generated by an abnormal air leakage.For identifying the passing pallet,a thermal-resistant type RFID technology was adopted.Based on the measured data via anemometers,the air leakage rate of sintering machine was calculated with the mass balance method,and pallets with the abnormal leakage can be detected and ranked in the severity of leakage from the measured sound pressure with the relevant criteria.In addition,for examining the leakage situation,this study set up a capillary type of differential pressure gauge to double cone valve(DCV)below the electrostatic precipitator(EP)in sintering plant for collecting the larger dust.The criteria of determining leaked DCV and the patterns for replacing the DCV were proposed to develop a detecting and predicting system on the air leakage into dust collectors of sinter machine.It offered field staff a basis of maintaining or renewing DCV via a warning reminding and reducing air leakage to increase EP efficiency for avoiding the dust emission from the stack.These technologies had been implemented in the sintering plants of China Steel Corporation,and they can effectively reduce the air leakage rate by5%at least and further decrease the electricity consumption of the suction fan and coke rate,increase the production for the sintering machine.

基于CsPbBr_(3)量子点的湿度传感器的制备及其在微裂纹检测中的应用

采用简单的溶液路线结合乙酰丙酮铁(AAI)改性技术,制备了分散性良好、尺寸均匀的钙钛矿型CsPbBr_(3)-Fe量子点(QDs),通过X射线衍射(XRD)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)以及光致发光(PL)光谱对材料进行表征。并通过旋涂法构筑基于钙钛矿QDs的电阻式湿度传感器。湿敏特性测量结果表明:该湿度传感器在10%~100%的相对湿度(RH)的检测范围内具有良好的线性、快速响应特性;在70%RH时传感器的灵敏度为1.1,响应时间为33 s,恢复时间为38 s。此外,我们运用有限元方法模拟了管道微裂缝的泄漏行为,通过模拟不同流速、压力下的流速场和压力场分布,验证了利用湿度传感器检测泄漏点位置的可行性。

填埋场渗漏磁法检测的噪声来源与剔噪方法综述

填埋是固体废物集中处置的主要手段,填埋场则是固体废物对地下水环境影响集中发生的场所。通过防渗层HDPE膜漏洞的渗漏是导致地下水污染的主要途径和方式,开展防渗层渗漏快速检测和精准定位具有重要意义。传统电学渗漏检测难以检测双层衬垫或多层衬垫系统的下层衬垫破损,基于传统电法改进的电磁方法展现出下层衬垫检测的潜力,但迫切需要解决填埋场复杂服役人文环境和地球磁场环境下的电磁噪声干扰问题。为此,通过对磁法检测原理和信号特征的深入分析,以及对背景磁噪声类型、来源、特征和电磁噪声去噪方法的综述,得出如下结论:在特定条件下,漏洞越近,其磁场信号越强;填埋场噪声主要来源于地磁和人文磁场,且不同噪声有不同特征,需不同处理;去噪过程先预处理排除干扰,其次在检测中选择适宜的滤波或测量法去除噪声。

基于VMD的广义三次互相关管道泄漏定位检测

针对天然气管道泄漏检测声波定位技术中,二次互相关时延估计算法存在较大误差的问题,提出了一种基于变分模态分解(VMD)结合广义三次互相关的时延估计算法。该方法首先利用VMD算法对两路信号进行分解并重构信号;其次,在二次互相关的基础上再进行一次相关,并在互相关算法的峰值检测阶段引入希尔伯特变换(HT),对峰值进行尖锐化处理,成为一种新型的广义三次互相关时延估计算法。通过对平台搭建的油气管道泄漏检测系统采集数据进行模拟试验,分析了各算法的精度。试验表明,相较于二次互相关,改进广义三次互相关时延估计算法定位平均精度有明显的提升,有着更高的精度和更好的抗噪性能,在天然气管道泄漏定位方面有着更广泛的应用前景。