基于声发射理论的安全阀气体内漏信号特征研究

基于声发射理论,研究安全阀气体内漏情况下的信号特征。设计搭建了安全阀内漏模拟试验台和安全阀内漏声发射信号检测系统,通过理论分析和试验研究的方法,模拟了安全阀密封面划伤、密封面出现单个漏孔、密封面出现多个漏孔等内漏情况,研究了这些情况下声发射信号的频谱特征,泄漏率对于声发射信号特征参数平均信号电平(ASL)的影响,以及泄漏率与平均信号电平之间的量化关系,为实现安全阀内漏在线监测奠定基础。     

基于声学检测系统理论的输油管道阀门内漏问题分析

针对阀门内漏在线诊断问题,建立了一套阀门内漏在线检测系统,根据声发射技术原理,基于不同压力和不同开度条件下,检测了声发射的各种参数随泄漏率的变化,并运用Parseval原理进行了不同泄漏率的声发射参数拟合,并从频域角度对不同开度下声发射信号进行功率谱密度分析。研究结果表明,内漏的声发射参量会随压力和开度的变化而改变,峰值段的功率谱密度会随开度的增加而向低频段移动,利用声发射参量RMS模拟的内漏率更接近试验内漏率,灵敏度比其他声发射参量的模拟值高。     

基于声发射的阀门泄漏检测及标定方法的研究

基于阀门泄漏产生声发射信号的原理,建立了可用于测试阀门泄漏率低于0.1Pa.m3/s的微小泄漏状态的声发射检测系统,采用气泡法对阀门微小泄漏进行标定。针对声发射信号在检测中易受环境噪声干扰的问题,设计了信号调理电路来降低各种噪声,并编制了LabVIEW程序对采集的声发射信号进行分析和处理。以手动球阀为被测对象,在建立的系统上进行了大量泄漏检测实验,结果表明,该系统能快速方便地对阀门微小泄漏做出定性分析。     

油气管道球阀结构设计改进及性能试验研究北大核心CSCD

为了提高管道球阀内部密封可靠性,设计了一种采用具有组合密封特性的多重阀座结构,并开展了密封性能和寿命试验分析,考察了阀门压力试验、功能试验和现场工业性应用试验等过程。结果表明,0.6 MPa低压气密封和1.1倍公称压力高压气密封性能试验结果均符合标准规定的无可见泄漏要求;改进设计后的管道球阀具有独有的多级阻断介质密封、压力缓冲和全封闭注脂的特性和优势,提高了阀门的密封可靠性,根据多重阀座的特性,综合性能达到或优于2台传统球阀串联的叠加效果。研究结果为管道球阀尤其是关键位置阀门提供了更优的选择,降低了油气管道球阀内漏的风险,对保障管道系统安全运行具有重要的意义。     

水流由管路漏泄所激发的声发射

本文主要探讨用声发射检测管路漏泄的可能性。试验结果表明,管道中的水流由小孔漏泄,在达到一定流量时,可在管壁中激发声发射波。掌握此声发射的发生机理与传播规律,对于设备诊断技术是有实用意义的。     

油气管道球阀结构设计改进及性能试验研究

为了提高管道球阀内部密封可靠性，设计了一种采用具有组合密封特性的多重阀座结构，并开展了密封性能和寿命试验分析，考察了阀门压力试验、功能试验和现场工业性应用试验等过程。结果表明，0.6 MPa低压气密封和1.1倍公称压力高压气密封性能试验结果均符合标准规定的无可见泄漏要求；改进设计后的管道球阀具有独有的多级阻断介质密封、压力缓冲和全封闭注脂的特性和优势，提高了阀门的密封可靠性，根据多重阀座的特性，综合性能达到或优于2台传统球阀串联的叠加效果。研究结果为管道球阀尤其是关键位置阀门提供了更优的选择，降低了油气管道球阀内漏的风险，对保障管道系统安全运行具有重要的意义。     

基于声发射与小波变换的阀门泄漏研究

在实验室建立了一阶水箱循环系统,采用声发射技术与小波变换理论,在相同的实验条件情况下,进行了不同开度下阀门泄漏的声发射检测实验。通过matlab软件对原始电压信号进行去噪处理、频率分析、能量特征分析,能够准确判断管道有无泄漏,得出泄漏大小与声发射信号之间的关系。经对各频段能量特征分析,进一步验证声发射技术在阀门泄漏应用的可靠性。     

基于特征参数和功率谱分析的液压滑阀内漏声发射检测实验研究

内泄漏直接影响液压系统的工作效率，常用的检测方法主观性强、效率低。以5组通径16 mm的三位四通换向滑阀为研究对象，设计并搭建了液压滑阀内泄漏声发射检测实验台。对比声发射信号的幅值域参数大小和响应时间，确定了最佳测量点。结合实验数据，找到了压力、间隙高度对幅值域特征参数的影响规律。运用AR功率谱提取信号的特征频率，发现滑阀内漏的声发射信号基频为40 kHz，泄漏量越大，特征频率越向低频方向偏移；特征频率呈现倍频的关系，内漏滑阀的功率谱曲线有小幅波动，正常滑阀功率谱曲线光滑。所得实验数据和结论对构建滑阀内漏诊断数据库具有重要意义。     

基于小波包和HHT变换的声发射信号分析方法

针对声发射管道泄漏检测过程中的噪声干扰问题,对基于小波包和经验模态分解(EMD)的声发射信号处理方法进行了研究.采用小波包分解算法和经验模态分解都可以对管道泄漏声发射信号进行分解,但分解结果却存在一定区别.EMD是近年来非平稳信号分析领域的一个突破,对管道泄漏声发射信号进行EMD分解后,选择包含声发射特征的若干固有模式函数(IMF分量)进行重构,可以提取到管道泄漏声发射信号的本质特征,消除噪声信号的干扰.相对小波包分解方法而言,对根据IMF分量重构的声发射信号进行相关分析计算,得到的管道泄漏点的位置更为精确.     

基于主成分分析方法的磨削颤振的检测

介绍了应用特征参数有效性评价方法和主成分分析法选取并简化磨削加工过程中采集的原始声发射信号的特征参数,然后再通过模式识别的方法对所得出的声发射信号的主成分进行判别,以达到对磨削过程中颤振的实时检测.检测方法通过试验得到了验证,能够有效地区分磨削过程是否发生了颤振.     

Prediction method of ball valve internal leakage rate based on acoustic emission technology

Valves on natural gas transmission pipelines are prone to internal leakage due to the influence of environment, transportation medium, misoperation and other factors. Therefore, timely and accurate detection of valve internal leakage is of great significance to the safe operation of the pipelines. As a non-destructive testing method, acoustic emission (AE) technology can be used for online detection of internal leakage of valve. In order to improve the accuracy of AE technology for detecting valve internal leakage, a ball valve internal leakage detection method based on acoustic emission technology is proposed. Firstly, the AE signal generated by the internal leakage of the ball valve is detected by AE sensor, and the AE signal is denoised by wavelet packet threshold denoising. Secondly, the characteristic parameters with high correlation with the internal leakage rate of the ball valve are selected by using the correlation coefficient method to form the sample feature set. Finally, the mathematical model between the internal leakage rate of the ball valve and the characteristic parameters is established by using the sparrow search algorithm to optimize the backpropagation neural network, and to predict the internal leakage rate of the ball valve. An experimental platform is built to verify that the proposed method can predict the internal leakage rate of the ball valve more accurately. It provides strong technical support for the safe operation of natural gas transmission pipelines.     

Internal leakage rate prediction and failure diagnosis of buried pipeline ball valve based on valve cavity pressure detection

— Ball valve is a key fluid control equipment used extensively in oil and gas pipelines. The online detection and failure diagnosis of the internal leakage of the ball valve is of great significance to ensure the safety operation of natural gas transmission pipelines. This paper proposes a prediction method of the internal leakage rate and a diagnosis method of the failure mode of the buried pipeline ball valve based on valve cavity pressure detection. Firstly, the valve cavity pressure signal generated by the internal leakage of the ball valve is detected by the pressure sensor, and the valve cavity pressure signal is denoised by wavelet threshold denoising. Then, the back propagation (BP) neural network has the disadvantage of unstable learning ability, so the BP neural network is optimized by chaos sparrow search optimization algorithm (CSSOA-BP). Finally, the prediction model of the ball valve internal leakage rate and the diagnosis model of the ball valve failure mode are established by using CSSOA-BP neural network and the characteristic parameters of the valve cavity pressure signal. To verify the performance of the prediction model and the diagnosis model of CSSOA-BP neural network, the predictive results and diagnostic results are compared with those of the sparrow search algorithm optimization BP (SSA-BP) neural network and BP neural network. The experimental results show that the maximum prediction error of CSSOA-BP neural network is the smallest, which is 13.6%. The accuracy of the diagnostic results of CSSOA-BP neural network is the highest, which is 83.3%. It indicates that the proposed method can achieve better predictive results of the ball valve internal leakage rate and more accurate diagnostic results of the ball valve failure mode.     

Design and experimental research of internal leakage detection device of buried pipeline ball valve based on valve cavity pressure detection

Valve is a key control component in the oil and gas long-distance pipelines, and the internal leakage detection of the valve has important research significance and application value. In this paper, a new method for detecting internal leakage of the buried pipeline ball valve based on the pressure change of the valve cavity was proposed. Firstly, a simplified internal leakage model of ball valve was established, and the change law of valve cavity pressure under different openings was obtained by finite element simulation. Secondly, a set of detection device was designed based on the internal leakage principle of buried ball valve and an experimental platform of ball valve internal leakage was built. Then, the pressure signal of the ball valve cavity under different openings and different pressures was collected by the pressure sensor, and the relationship between the power source pressure, the opening and the time required for the valve cavity pressure to reach equilibrium was established. Finally, the internal leakage degree of ball valve was simulated by ball valve opening and the Back Propagation (BP) neural network was selected to invert the ball valve opening. The inversion results showed that the maximum relative error of the liquid and gas experiment were 13.4% and 19.1%, respectively. The inversion results also verified the feasibility of BP neural network in the inversion calculation of the internal leakage degree of the ball valve, which provides guidance for the study of the classification of the internal leakage degree. The experiment and inversion results show that the proposed method can realize the detection of the internal leakage of the buried ball valve, which provides application reference in practice.     

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

The detection of acoustic emission (AE) signals produced by liquid and gas leakage through valves can be related directly to the qualitative leakage rate. This allows for cost estimation of losses in processes for several industries. However, to find out the relationship between qualitative leakage rate and AE signal large amounts of experimental data is needed. This paper presents a theoretical investigation of the acoustic emission to detect the internal leakage rate through a valve and experimental validation. The AE signals generated by internal liquid and gas leakage through valves were characterised. The effect of the influenced factors of leakage rates, inlet pressure levels, valve sizes and valve types, on AE parameter, AE_RMS, were studied and explained. The results of theoretical and experimental showed that AE signal power computed from the power spectral density (PSD) correlated well with influenced factors of leakage rates. Finally, a novel and inexpensive AE instrument has been invented for predicting qualitative leakage rate using a micro processor and derived relationship.     

气体管道泄漏声源特性声发射试验研究

对气体管道泄漏孔处声源进行了声发射检测试验,分析了气体泄漏产生声发射的原因,通过对不同泄漏孔直径、不同泄漏内压情况下的声发射信号处理与分析,得出气体管道泄漏声源的频率范围及幅度随管道内部压力、泄漏孔径的变化影响规律,并与管道气体泄漏的数值模拟结果进行了对比分析,试验研究结果为气体管道泄漏声发射检测提供依据。     

输送天然气用球阀及油品管路用平板闸阀

叙述了输送天然气管路用球阀和输送油品管路用平板闸阀的设计和制造标准、材料及结构特点。     

改进LMD及高阶模糊度函数的管道泄漏定位

针对天然气管道泄漏定位的问题,提出一种基于改进局域均值分解(LMD)及高阶模糊度函数的时延估计方法。该方法首先采用改进的LMD对声发射信号进行分解,获得多个PF分量,进而提出根据K-L散度的PF分量自动选择算法,获取含有主要泄漏信息的PF分量,在此基础上,研究了基于高阶模糊度函数计算声发射信号的时频参数,并通过时频分析获取特征频率的到达时间差,最后结合泄漏产生的广义声发射信号的传播速度完成对天然气管道泄漏的定位。实验结果表明,提出的方法能够进行定位且精度较直接相关法明显提高。     

Numerical study on leakage detection and location in a simple gas pipeline branch using an array of pressure sensors

Pressure signals measured in gas pipelines provide useful information for monitoring the status of pipeline network operations. This study numerically investigates leakage detection and location in a simple gas pipeline branch using transient signals from an array of pressure sensors. A pipeline network simulation model was developed and used to predict one-dimensional compressible flow in gas pipelines. Two monitoring methods were tested for leakage detection and location; these include cross-correlation monitoring and pressure differential monitoring. The performance and reliability of the two monitoring methods were numerically assessed based on simulation results for a simple pipeline branch operation with a prescribed leakage rate (5% of total flow rate in the gas pipeline).