信息融合技术在管道泄漏检测与定位中的应用

针对传统的单一管道泄漏检测方法,提出基于多传感器信息融合技术的输油管道泄漏检测与定位方法.首先利用小波分析法对压力、流量进行去噪,并检测波形的奇异点;利用相关分析法来对小泄漏量进行检测,通过首末端压力的互相关函数得到时间延迟,进行泄漏点定位;根据Dempster-Shafer证据推理法把得到的数据作为证据,利用D-S法...     

Z形管不同位置泄漏的数值模拟

油气管道的泄漏易引发重大的安全事故并造成巨大的经济损失,特别是我国有些地区管网运行已有30多年,发生泄漏几率很大。及时发现管道泄漏事故并作出合理的解决方案,对控制事故的发展和减少由此带来的损失有着重要的意义。利用CFD仿真软件和有限容积法借助多相流VOF模型,研究了上下两个弯头的Z形管不同漏点的复杂工况,考虑了重力的影响,对其压力场进行了数值模拟,并对不同位置漏点对泄漏的影响做出了理论分析。     

液下气泡检漏方法研究

探讨了液下气泡检漏法的基本原理、泄漏率计算方法以及检漏精度和分辨率。应用该方法对石棉橡胶垫片的泄漏率进行了测定 ,试验结果与采用集漏空腔增压法测得的泄漏率具有较好的可比性。分析了液下气泡法用于工程实际时应注意的问题。     

Fault Detection and Diagnosis in a Sour Gas Absorption Column Using Neural Networks

Process fault detection and diagnosis is an important problem in plant control at the supervisory level. It is the central component of abnormal event management which has attracted a lot of attention recently. In this study, the use of artificial neural networks (ANN) for fault detection is explored. An ANN can represent nonlinear and complex relations between its inputs (sensor measurements) and outputs (faults). As a test case, absorption of CO₂ gas in monoethanolamine (MEA) by a pilot plant called “automatic absorption and stripping pilot plant” is studied. For detecting and diagnosis of faults, variations in feed rate, feed composition, liquid absorber rate and composition are imposed onto the plant. The faults in this process influence variables such as the composition of absorbed gas (CO₂) and temperature and pressure drop of the column. The CO₂ concentration in the product should not exceed a certain limit. By selecting a proper architecture for the network (5‐9‐10), it is possible to detect the faults accurately. The network is trained using the back propagation method. The developed fault diagnosis algorithm is tested using data that has not been seen by the network.     

基于BP神经网络的煤直接液化装置VOCs包袋法检测与预测建模

挥发性有机物（VOCs）是现代煤化工生产过程中产生的主要污染物之一。煤化工是我国石化领域的重要组成部分，通过煤制油装置可以实现煤炭的清洁利用，煤制油装置由于设备、物料、操作条件等与石化企业存在差异，目前我国针对石化的美国联邦环保署（EPA）相关方程不适用于煤直接液化装置，且目前国内并没有用于核算煤制油装置的相关方程。本文开展了煤制油装置VOCs检测方法研究，并修正了相关方程系数，得到了密封点VOCs核算方程。首先对煤制油装置进行泄漏与维修（LDAR）检测，得到了煤制油装置密封点的VOCs泄漏数据。在数据分析的基础上，改进了包袋法采样方法，对煤直接液化装置进行了密封点的包袋采样分析。在此基础上，得到了相关方程的修正系数，首次提出了适用于国内煤制油装置VOCs泄漏核算的相关方程。最后，考虑了移动距离、温度、压力及多处泄漏等参数对相关方程的影响，校准了煤直接液化装置的相关方程，建立了反向传播算法（BP）神经网络模型，得到了逼近结果较好的相关函数。     

Long range pipeline leak detection and localization using discrete observer and support vector machine

A realistic pipeline modeled by a nonlinear coupled first-order hyperbolic partial differential equations (PDEs) system is studied for the long transportation pipeline leak detection and localization. Based on the so-called water hammer equation, a linear distributed parameter system is obtained by linearization. The structure and energy preserving time discretization scheme (Cayley–Tustin) is used to realize a discrete infinite-dimensional hyperbolic PDEs system without spatial approximation or model order reduction. In order to reconstruct pressure and mass flow velocity evolution with limited measurements, a discrete-time Luenberger observer is designed by solving the operator Riccati equation. Based on this distributed observer system, data on different normal and leakage conditions (various leak amounts and positions) are generated and fed to train a support vector machine model for leak detection, amount, and position estimation. Finally, the leak detection, amount estimation, and localization effectiveness of the developed method are proved by a set of simulations. © 2019 American Institute of Chemical Engineers AIChE J, 65: e16532 2019     

Shape anomaly detection for process monitoring of a sequencing batch reactor

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. Classical methods for data validation are quantitative in nature and require either accurate process knowledge, large representative data sets, or both. In contrast, a small section of the fault diagnosis literature has focused on qualitative data and model representations. The major benefit of such methods is that imprecise but reliable results can be obtained under previously unseen process conditions. This work continues with a line of work focused on qualitative trend analysis which is the qualitative approach to data series analysis. An existing method based on shape-constrained spline function fitting is expanded to deal explicitly with discontinuities and is applied here for the first time for anomaly detection. An experimental test case and a comparison with the principal component analysis method bear out the benefits of the qualitative approach to process monitoring.     

基于LSTM时间序列重建的生产装置异常检测

工业生产装置通常设置传感器报警阈值进行报警，但是对处于报警阈值以下的时间序列异常难以及时捕捉。基于统计的传统检测方法在解决时间序列异常检测上存在很大挑战，因此提出基于long short term memory (LSTM)时间序列重建的方法进行生产装置的异常检测。该算法首先引入一层LSTM网络对传感器数据的时间序列进行向量表示，采用另一层LSTM网络对时间序列进行逆序重建，然后利用重建值与实际值之间的误差，通过极大似然估计方法对该段序列进行异常概率估计，最终通过学习异常报警阈值实现时间序列异常检测。采用ECG测试数据、能源数据与危险品储罐传感器数据进行了仿真实验，验证了所提方法在不同长度的数据上的有效性。     

基于支持向量机的输气管道泄漏压降信号智能识别方法

输气管道干线气液联动阀根据检测管道压降速率、持续时间判断管道是否发生泄漏和自动关断阀门。该方法难以识别小孔泄漏等压降速率低于关断阈值的事故工况和压缩机抽吸等正常运行工况。以相国寺储气库集注干线为对象,通过仿真获得与管道泄漏、压缩机抽吸及截断阀紧急截断3种工况相关的压降速率信号,基于支持向量机建立了管道泄漏信号识别模型。提出混沌映射与自适应惯性权重的教与学优化算法,获得了模型中惩罚因子C和核函数参数g的最优值。利用相国寺储气库铜相线600组数据验证表明,优化后的模型：①对3种工况识别准确率为98.5%,较优化前提升了4.2%;②对于当量直径为50~125mm的小孔泄漏识别准确率为100%,提升了对小孔泄漏信号识别的准确性;③对压缩机抽吸和截断阀紧急截断工况识别的准确率分别为96.7%和100%;④当泄漏孔径小于50mm、压降速率小于0.01MPa/min时,阀室检测到的压降速率信号特征相近,此时建议使用气液联动阀与SCADA系统监测数据综合判断。     

Decentralized Fault Isolation by a Combination of Transfer Entropy and Classification Methods

Early fault detection and isolation in industrial systems is vitally necessary to prevent any potential product damage. The paper proposes a new decentralized multi-unit fault isolation methodology in which all the known process faults with similar time signatures are grouped into appropriate categories. An innovative genetic algorithm-based method is introduced to explore for optimum plant zones in a large-scale plant wide search to appropriately configure each architectural unit, having less reliance on excess process variables with redundant and uncorrelated diagnostic information. The methodology employs a set of Bayes and radial basis function neural network classifiers to properly isolate the most usual known faults. A new idea based on transfer entropy algorithm has been integrated in the decentralized configuration to be triggered for isolation of novel faults which have been left unrecognized by the set of maintained classifiers. Experimental results clearly demonstrate that the proposed methods are considerably superior to the conventional centralized methods.     

Neural network-based source tracking of chemical leaks with obstacles

The leakage of hazardous gases poses a significant threat to public security and causes environmental damage. The effective and accurate source term estimation (STE) is necessary when a leakage accident occurs. However, most research generally assumes that no obstacles exist near the leak source, which is inappropriate in practical applications. To solve this problem, we propose two different frameworks to emphasize STE with obstacles based on artificial neural network (ANN) and convolutional neural network (CNN). Firstly, we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark dataset. Secondly, we define the structure of ANN by searching, then predict the concentration distribution of gas using the searched model, and optimize source term parameters by particle swarm optimization (PSO) with well-performed cost functions. Thirdly, we propose a one-step STE method based on CNN, which establishes a link between the concentration distribution and the location of obstacles. Finally, we propose a novel data processing method to process sensor data, which maps the concentration information into feature channels. The comprehensive experiments illustrate the performance and efficiency of the proposed methods.     

基于瞬变流的输油管道泄漏检测模拟

针对输油管道泄漏检测的现状进行了总结,并研究了一种基于瞬变流的检测输油管道泄漏点的方法,该方法利用管道均匀竖直的特点,在下游流体出口处设置阀门和压力检测装置,当输油管道发生泄露时,周期性关闭阀门产生瞬变流即连续压力波,以连续压力波为输入信号,泄漏管道为系统,检测管道出口处的压力信号为输出信号来检测泄漏。当产生同样周期及振幅的连续压力波时,由于泄漏点所在的位置不同及压力波造成管道共振,输出信号即管道出口检测到的压力也不同,根据这一特性,不断改变输入的压力波周期,即可产生不同的系统频率响应图。由此判断出泄漏点位置。     

城镇燃气管道泄漏事故树风险分析

城市燃气管道主要分布在人口和建筑物集中的区域,一旦破坏发生泄漏将对周围的人员及财产造成严重威胁。因此,有必要对其威胁进行风险分析。通过应用一种重要而常用的系统安全分析方法—事故树分析法(FTA)对城镇地下燃气管道遭受第三方破坏所致的泄漏事故进行了分析,建立了相应事故树模型。采用布尔代数化简法计算出事故树最小径集并对各基本原因事件的结构重要度进行排序。结合最小径集包含的各事件及结构重要度排序分析,可以确定事故管理的主要对象,并提出减少事故的措施,为确定安全对策提供可靠依据。     

高压氨泵干气密封泄漏的故障分析与处理对策

分析多级离心式高压氨泵干气密封损坏泄漏的原因,是机封紧定套失效使机封内侧动环传动轴套和定位轴套外移,动、静环比压加大,磨损加剧,密封面损伤而泄漏.采取在车床上单边进刀量为0.25 mm的整形光刀措施,可继续使用.     

输油弯管泄漏数值模拟

我国现有输油管道经过多年冲刷及内外腐蚀后,存在一定的安全隐患,对管道的安全运行构成了严重危害.利用CFD仿真软件,依据有限容积法,采用多相流VOF模型对不同输送条件及泄漏孔径对泄漏后管内压强分布进行了模拟.研究表明:当泄漏孔径相同时,随着输送速度的增加,管内压强整体增大,泄漏口下游存在增大的高压区;当输送速度相同时,泄...     

氦质谱检漏法应用研究

对氦质谱检漏法的检漏精度、分辨率、检漏范围以及检测结果的重复性进行了系统研究 ,阐述了影响检漏精度的可能因素。在真空密封试验台上对石棉橡胶垫片等密封元件的检漏结果表明 ,该检漏方法的检测精度高、重复性好 ,特别适用于微小泄漏率的精确检测 ,是压力容器和密封元件的理想检漏方法。     

重整装置火灾和爆炸事故树分析

对某石化企业重整装置进行了系统分析,确定其为重大危险源。运用事故树分析法实施风险评价,根据危险性分析结果和物质特性确定了顶事件为火灾、爆炸事故,建立事故树并求出最小径集和各基本事件的结构重要度。结果表明,防护明火措施不足、设备温度过高以及设备泄漏是引起事故的主要原因。提出了相应对策,以改善装置的安全性和运行可靠性。     

管道电磁探伤技术研究及展望

介绍了电磁探伤中的漏磁检测系统及其检测原理和基本结构,定性分析了管道缺陷外形尺寸与形状对漏磁场的影响规律,为漏磁检测缺陷信号研究和实际检测工作提供参考。提出了应用有限元技术、小波消噪、神经网络及多传感器数据融合技术等对管道漏磁检测信号进行处理的方法,指出了漏磁检测需要进一步研究的内容。     

声发射技术在化工设备状态监测与故障诊断中的应用

介绍了高压加热器泄漏和汽轮机组动静摩擦等故障所产生的声发射信号的特点 ,并通过几个实例说明了利用声发射技术综合诊断泄漏和摩擦故障的方法 ,同时介绍了声发射技术在这方面应用的优越性和实用性     

供水管道泄漏检测与定位技术应用研究

基于瞬时压力监测的负压波法,应用相关分析理论对供水管道进行泄漏检测与定位.以MAT-LAB和组态王为软件开发平台,对信号进行时域互相关分析,由组态王生成友好的人机交互界面,MAT-LAB完成复杂算法运算,二者采用DDE方式进行通信.对于检测到的泄漏信号中含有的大量噪声,采用滑动平均或小波分析等方法进行去噪处理.根据具体...