Application of an Improved ISODATA Method to Pipeline Leak Detection

Increasingly serious leak problem in pipeline transportation has not only affected the operation of pipelines but also caused loss of precious resource and environmental damage. Based on the analysis of the occurrence of negative pressure waves and the unsupervised learning of pattern recognition, the Interactive Self-organizing Data Analysis Technique Algorithm （ISODATA） method was used to classify the negative pressure waves and then the states of pipelines could be determined. K L transformation was used to eliminate the correlativity of feature parameters and to reduce the dimensionality of feature vector space to speed up calculation. Experimental results validated the accuracy and practical value of this method.

Leakage Detection in Water Distribution Network Based on a New Heuristic Genetic Algorithm Model

The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage detection are not only expensive and time consuming, but also have a low efficient. As a result, the global leakage detection methods such as leak detection based on simulation and calibration of the network have been considered recently. In this research, leak detection based on calibration in two hypothetical and a laboratorial networks is considered. Additionally a novel optimization method called step-by-step elimination method (SSEM) combining with a genetic algorithm (GA) is introduced to calibration and leakage detection in networks. This method step-by-step detects and eliminates the nodes that provide no contribution in leakage among uncertain parameters of calibration of a network. The proposed method initiates with an ordinary calibration for a studied network, follow by elimination of suspicious nodes among adjusted parameters, then, the network is re-calibrated. Finally the process is repeated until the numbers of unknown demands are equal to the desired numbers or the exact leakage locations and values are determined. These investigations illustrate the capability of this method for detecting the locations and sizes of leakages.

Quantitative risk assessment & leak detection criteria for a subsea oil export pipeline

A quantitative risk assessment （QRA） based on leak detection criteria （LDC） for the design of a proposed subsea oil export pipeline is presented in this paper. The objective of this QRA/LDC study was to determine if current leak detection methodologies were sufficient, based on QRA results, while excluding the use of statistical leak detection; if not, an appropriate LDC for the leak detection system would need to be established. The famous UK PARLOC database was used for the calculation of pipeline failure rates, and the software POSVCM from MMS was used for oil spill simulations. QRA results revealed that the installation of a statistically based leak detection system （LDS） can significantly reduce time to leak detection, thereby mitigating the consequences of leakage. A sound LDC has been defined based on QRA study results and comments from various LDS vendors to assist the emergency response team （ERT） to quickly identify and locate leakage and employ the most effective measures to contain damage.

Study of pressure effects on ocean in-situ detection using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) has attracted extensive attention as a new technique for in-situ marine application. In this work, the influence of deep-sea high pressure environment on LIBS signals was investigated by using a compact LIBS-sea system developed by Ocean University of China for the in-situ chemical analysis of seawater. The results from the field measurements show that the liquid pressure has a significant effect on the LIBS signals. Higher peak intensity and larger line broadening were obtained as the pressure increases. By comparing the variations of the temperature and salinity with the LIBS signals, a weak correlation between them can be observed. Under high pressure conditions, the optimal laser energy was higher than that in air environment. When the laser energy exceeded 17 mJ, the effect of laser energy on the signal intensity weakened. The signal intensity decreases gradually at larger delays. The obtained results verified the feasibility of the LIBS technique for the deep-sea in-situ detection, and we hope this technology can contribute to surveying more deep-sea environments such as the hydrothermal vent regions.

Pipeline Potential Leak Detection Technologies: Assessment and Perspective in the Nigeria Niger Delta Region

This paper examines the advances in pipeline third party encroachment alert systems and leak control methods in the oil/gas industry. It also highlights the extent of spill/pollution issues in the Niger Delta region due to intended/unin- tended damages and suggests a possible method of control. It is believed that the best option to avoid pollution due to pipeline failure is to ensure that hydrocarbon does not exit from the pipeline. With the different methods considered in this review, acoustic monitoring of change in the operational sound generated from a given pipeline section is suggested to be practicable to identifying sound abnormalities of third party encroachments. One established challenge of the acoustic system for buried pipelines protection is attenuation of acoustic transmission. An attempt to check the performance of an acoustic transmission on steel pipelines submerged in water points to a similar research on plastic water pipelines that attenuation is small compared with pipe buried in soil. Fortunately, Niger Delta of Nigeria is made of wetland, swamps and shallow water and could therefore offer an opportunity to deploy acoustic system for the safety of pipelines against third party attacks in this region. However, the numerous configuration and quantity of oil installation in this region imply that cost of application will be enormous. It is therefore suggested that a combination of impressed alternating cycle current (IACC) which traces encroachment on the pipeline coating and an acoustic system be used to manage intended and unintended pipeline potential damages. The IACC should be used for flow lines and other short distance delivery lines within the oilfield, while the relatively large diameter and long length delivery, trunk and transmission lines should be considered for acoustic protection. It is, however, noted that further efforts are required to reduce cost and improve effectiveness of these systems.

Leak Detection in Offshore Pipelines of Conveying Fluid

Leakage from pipelines has caused serious environmental pollution and economic losses. Usually, leak detection can reduce the damage. The paper mainly discusses a hydraulic gradient-based leak detection method. The basic idea is outlined first, followed by a description of a laboratory experiment in a water pipeline. Several pressure curves are established based on different leak locations under the condition of a constant total flow rate. It is demonstrated that the leak of a large leak quantity can be detected reliably by the hydraulic gradient method.

Leak Detection of Gas Pipelines Based on Characteristics of Acoustic Leakage and Interfering Signals

When acoustic method is used in leak detection for natural gas pipelines,the external interferences including operation of compressor and valve,pipeline knocking,etc.,should be distinguished with acoustic leakage signals to improve the accuracy and reduce false alarms.In this paper,the technologies of extracting characteristics of acoustic signals were summarized.The acoustic leakage signals and interfering signals were measured by experiments and the characteristics of time-domain,frequency-domain and time-frequency domain were extracted.The main characteristics of time-domain are mean value,root mean square value,kurtosis,skewness and correlation function,etc.The features in frequency domain were obtained by frequency spectrum analysis and power spectrum density,while time-frequency analysis was accomplished by short time Fourier transform.The results show that the external interferences can be removed effectively by the characteristics of time domain,frequency domain and time-frequency domain.It can be drawn that the acoustic leak detection method can be applied to natural gas pipelines and the characteristics can help reduce false alarms and missing alarms.

Leak Detection in Water Distribution Systems Using Bayesian Theory and Fisher’s Law

A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.

Performing Reliable Early Leak Detection on Storage Tank Terminals:Retrofitting of Existing Facilities and New Buildings

Liquid leak detection may represent a challenge for Oil&Gas operators,as indicated by operational feed-back and independent studies.Despite the availability of many different leak detection technologies,some systems may either fail to detect spills or generate frequent false alarms.In particular,possible soil contamination from pre-existing leaks and pollution carry-over by rain water is difficult to filter out by a leak sensing system.Typical case of false alarms relates to punctual sensors installed upstream the drain valve within the storage tank bunds,monitoring possible presence of leaks in rain water.Besides old soil contamination,other criteria should also be considered when selecting a spill detection technology,such as asset type to be monitored(storage tank,pipeline,…),system accuracy(minimum detectable quantity,ability to localize the leak),detection time,reliability over time,capital,installation and operating costs.The paper will include an evaluation of different external leak detection technologies with respect to the above-mentioned criteria,pointing out the capabilities and limitations of each system.Focus will be placed on reliability of leak monitoring systems in challenging environments.A new generation of digital,reusable sensing cables and probes,as well as the impact of sensitivity for different applications,will be discussed.Since leak sensor installation environment(positioning,adoption of special precautions,…)may significantly affect the system performance,different above ground and underground configurations will be presented,both for new builds and existing facilities.

In-Line Inspection of Pipeline Defects Detection Using Ring-Type Laser

Pipeline plays a vital role in transporting fluids like oils, water, and petrochemical substances for longer distances. Based on the materials they carry, prolonged usage may cause the initiation of defects in the pipeline. These defects occur due to the formed salt deposits, chemical reaction happens between the inner surface and the transferring substance, prevailing environmental conditions, etc. These defects, if not identified earlier may lead to significant losses to the industry. In this work, an in-line inspection system utilizes the nondestructive way for analyzing the internal defects in the petrochemical pipeline. This system consists of a pipeline inspection robot having two major units namely the visual inspection unit and the power carrier unit. The visual inspection unit makes use of a ring-type laser diode and the camera. The laser diode serves as a light source for capturing good quality images of inspection. This unit is controlled by the Arduino in the power carrier unit which provides the necessary movement throughout the pipe. The inspected images captured by the camera are further processed with the aid of NI vision assistant software. After applying the processing function parameters provided by this software, the defect location can be clearly visualized with high precision. Three sets of defects are introduced in a Polylactide (PLA) pipe based on its position and angle along the circumference of the pipe. Further, this robot system serves as a real-time interactive image synchronization system for acquiring the inspected images. By comparing the actual and calculated defect size, the error percentage obtained was less than 5%.

Detection and analysis of gas tightness of continuous annealing furnaces

When a cold rolled strip is being treated in a continuous annealing furnace which is full of protective gas, the gas tightness of the furnace body, the connected facilities and the gas channels become an important indicator that directly affects the product＇s surface quality and shows the technical level of the design, the manufacture and the installation. By considering the problems of the gas tightness of a vertical annealing furnace in the installation and maintenance, this thesis evaluates the gas tightness indicator and gas tightness related level of the furnace body and the circulation duct, while studying and analyzing the technologies of negative-pressure leak detection and sealing.

炼化企业挥发性有机化合物减排的探索与实践

炼化企业加工过程有组织、无组织排放的挥发性有机化合物(VOCs)量较大,对大气环境中的颗粒物、臭氧浓度等产生重大影响。A公司自“十三五”以来对VOCs减排进行了探索与实践,通过采取油气回收、储罐浮盘高效密封改造、呼吸阀改型升级、无组织排放改为有组织回收、VOCs焚烧、常态化泄漏检测与修复工作等多种减排措施,VOCs排放量已从2015年的4230 t下降到2022年的2157 t,降低比例达到49%,取得了显著的成效。

基于迁移学习的气体泄漏红外图像去噪方法

非制冷型红外相机由于其成本低、寿命长、性能稳定等优势在气体泄漏检测领域有着广泛应用,而良好的图像去噪算法可以有效提升其检测灵敏度与准确性。结合深度学习和迁移学习技术,提出了一种基于深度迁移学习的气体泄漏红外图像去噪方法。首先使用静止场景数据集对卷积神经网络模型进行训练,然后固定部分模型参数,并通过仿真气体数据集对模型再次训练,最终获得适用于气体泄漏红外图像去噪的模型。实验结果表明,该方法可以对非制冷型红外相机拍摄的气体红外图像进行去噪,去噪后的图像具有明显的气体轮廓信息,同时可以分辨出泄漏源的位置。因此,该方法可以帮助非制冷型红外相机更好地完成气体泄漏检测任务。

声发射法检测输气管道泄漏的研究综述

声发射法检测管道泄漏是目前的热点方法,研究涉及泄漏信号的产生与传播、信号拾取与处理、基于信号的智能识别与定位等。通过介绍声发射检测定位原理及一些基本规律,详细分析小波分析、局域均值分解、经验模态分解、变分模态分解在处理泄漏信号时的优劣,探讨人工智能在处理管道泄漏声发射信号中的应用,总结管道泄漏的模态研究、相关试验研究,来理清基于声发射的管道泄漏检测现状及展望未来发展。

基于交叉注意力的多源数据融合的气体泄漏检测

为解决单一传感器在管道气体泄漏检测时容易出现误报、漏报的问题,及时预警并反馈泄漏状况,提出一种基于交叉注意力的多源数据融合管道泄漏检测方法。首先,利用预训练的ShuffleNetV2模型提取热像仪数据的空间特征;然后,结合一维卷积神经网络(1DCNN)和双向门控循环单元(BiGRU),构建1DCNN-BiGRU模型,以提取气体传感器数据的时序特征;最后,运用交叉注意力捕获数据的时空关联性得到2个数据源的特征表示,通过残差方式进行特征连接后输入到分类层中,得到识别结果。结果表明:所构建的多源数据融合模型(SCGA)对气体识别准确率为99.22%,损失值在0~0.04内波动;与仅使用气体传感器数据的支持向量机(SVM)、1DCNN、BiGRU模型相比,准确率至少提升4.12%;与仅使用热图像传感器数据的MobileNetV3、ShuffleNetV2、ResNet18模型相比,准确率至少提升1.14%;与将时序特征和空间特征直接拼接的多源数据融合模型(SCG)相比,准确率提升1%。SCGA模型对气体识别具有较高精度。

声波法在输油管道上的可检测泄漏率分析

为了确保输油管道出现泄漏时能够及时检测到泄漏,对声波法在输油管道上的可检测泄漏率进行了分析。首先,建立了声波产生和传播衰减模型,根据模型分析了声波在管道内的衰减性,得到管道两端声波衰减幅值的计算方法;其次,采用基于互信息优化的自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,简称CEEMDAN)和互谱分析相结合的去噪算法对采集的信号进行去噪,并实验验证了衰减模型可准确估计声波的衰减幅值,去噪算法可以有效消除信号中的噪声;最后,分析了管道特性与声波法在输油管道上的可检测泄漏率之间的关系。研究表明,管道特性在一定程度上决定了声波法的可检测泄漏率,声波法在输油管道上的最小可检测泄漏率可达0.45%。

基于TDLAS技术的甲烷气体泄漏成像检测

传统的甲烷气体泄漏检测方法主要以单点测量和红外热成像为主。前者由于测量点密度小且气体易于流动,很难确定泄漏点;后者根据气体云团与周围环境温度差异检测成像,温差较小时,成像对比度低、分辨力弱。根据以上局限性,提出一种甲烷气体泄漏成像检测技术。该技术结合TDLAS(Tunable diode laser absorption spectroscopy)遥测技术与激光主动成像方法对气体泄漏区域进行主动探测,然后采用SSIM(Structural Similarity)与混合高斯背景建模法相结合的算法对气体泄漏区域进行提取,实现对甲烷气体泄漏气团的成像检测。实验中对甲烷气体在不同泄漏流量、不同视频记录长度、不同环境下进行了检测。实验表明,该技术可实现对甲烷气体泄漏的主动成像检测,并且成像清晰、检测率高、实时性好、可准确呈现气体泄漏点。

直埋热水供热管道泄漏定位检测实验研究

选用傅里叶滤波法和小波阈值法进行了管道泄漏信号降噪和泄漏定位的对比研究。在DN300管路的实验直埋热水供热系统上进行了泄漏检测实验,采用小管径支管和调节阀模拟泄漏点、加速度传感器测量声波振动信号,依次选用2种滤波方法对直埋热水供热管道泄漏的声波振动信号进行数据降噪处理,讨论了泄漏信号降噪前后的信号频域特征和定位精度。在北京市某小区直埋供热管道进行了声波降噪泄漏检测工程验证,定位偏差不超过1.0 m。研究表明,傅里叶滤波法和小波阈值法对直埋热水供热管道泄漏定位的声波振动信号降噪处理效果显著,小波阈值法保持了全频段的信号特征,可获得更高的定位精度。

温度对石墨烯真空标准漏孔漏率的影响研究

石墨烯真空标准漏孔是基于石墨烯衍生材料研制的新型真空标准漏孔,其漏率下限低于一般的石英渗氦型标准漏孔,可用于超灵敏度检漏系统的校准。基于石墨烯的材料特性,石墨烯真空标准漏孔的漏率会随温度变化。本文针对温度对石墨烯真空标准漏孔漏率的影响进行了研究,通过测量漏孔在不同温度环境下的漏率,得到该种漏孔的温度系数与变化规律。研究结果表明,石墨烯真空标准漏孔的漏率随温度呈线性变化,且温度系数低于2.5%/℃。

融合结构重参数化变换的气体泄漏红外检测

针对常规工业气体泄漏检测装置需泄漏扩散到一定范围并与传感器接触时才能响应的不足,提出一种融合结构重参数化变换的红外非接触式检测网络模型GRNet。GRNet模型采用Mosaic-Gamma变换的预处理方法增加泄漏样本数量并提高图像对比度以增强模型的鲁棒性;通过K-means聚类分析出适用于气体泄漏红外检测的候选框以预置模型参数;优化定位损失函数以提高模型对泄漏区域的定位准确性;采用改进后的轻量化网络RepVGG模块重构特征提取网络增强模型的特征提取能力,以实现轻量化并提高检测精度。实验结果表明,GRNet模型对氨气泄漏的平均检测精度达到94.90%,单张图像平均检测时间达到3.40 ms。采用伪色彩映射实现泄漏浓度的视觉感知效果,采用PyQt5将GRNet模型进行封装实现气体泄漏红外检测系统界面的可视化并在Jetson Nano B01嵌入式实验平台部署该模型,验证了实际工程应用的可行性和有效性,为开发气体泄漏非接触探测装置以保障涉气企业的安全生产和稳定运行提供一种有效的检测算法。