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。     

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.     

Smart portable noninvasive instrument for detection of internal air leakage of a valve using acoustic emission signals

Acoustic emission (AE) can be used to detect and determine the internal leakage rate through a valve in many applications. However, a general AE data acquisition system is expensive and bulky. This paper presents a novel low-cost instrument based on microcontroller and a novel theoretical model based on AE technique to predict the leakage rate. The system is an embedded system instead of a general PC-based data acquisition. AE_RMS parameter is used to infer the leakage rate, and the effects of various process variables on the model are also studied. The experimental results have shown that the instrument is capable of detecting possible valve leakage encountered in online operation. With its portability, ease of use and compactness, the proposed system provides faster and low cost valve leakage detection.     

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。     

天然气管道球阀内漏发声机理及检测试验

球阀作为高压天然气输送管道的主要设备,其内漏时的喷流气体会产生声发射信号,通过研究该声发射信号特征规律将有助于阀门内漏流量量化检测。针对这一问题,进行了天然气输送管道球阀内漏发声机理和检测试验研究,分析了阀门内漏声发射现象产生的机理和内漏流量检测评价方法。在此基础上,应用声发射检测系统对3种不同尺寸内漏球阀进行了检测试验,通过试验分析了球阀在不同内漏流量下的声发射信号频谱特征分布规律,并采用小波包分析方法进行信号特征参数(信息熵、均方根、频域峰值)提取。拟合特征参数与内漏流量关系曲线,采用R~2(确定系数)指标对曲线拟合程度进行评价,评价结果表明,采用均方根值(root mean square,简称RMS)的曲线拟合程度最高(R2为0.979),可以用于天然气输送管道球阀内漏流量的量化检测。     

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。     

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。     

A relative calibration method for a valve leakage rate measurement system

This paper presents a relative calibration method for an internal valve leakage rate measurement system using both microcontroller and acoustic emission (AE) methods. An air jet was selected to calibrate because it provides a frequency spectrum similar to the AE spectrum obtained from the valve leak, especially in the frequency range of 100-300 kHz. Three AE sensors mounted on a valve were used to validate the system calibration. Ratios between the average energy (AErms) were obtained from each pair of sensors, and these ratios were used to transfer the leakage AErms value. Subsequence, its performance to predict the leakage rate in the laboratory and in the field was tested. The error is less than 5% in almost cases. Its benefit is to reduce the recalibration time when a part of measurement system is changed. Accordingly, inexpensive equipment including an AE sensor was built, and its system performance was revealed.     

A study of hydraulic seal integrity

The work described in this paper involved on-line detection of seal defects in a water hydraulic cylinder. An obvious effect of seal defect is internal leakage. Therefore, the approach used was to detect the internal leakage using suitable technique. The technique used involved detecting the acoustic emission (AE) due to the internal leakage. This paper evaluated various parameters of AE signals in terms of their capability in estimating the internal leakage rate in a water hydraulic cylinder. Experiments were carried out to study the characteristics of AE parameters at different internal leakage rates, the parameters including the root-mean-square (rms) value, the count rate, the peak magnitude of power spectral density and the energy. The correlations between these parameters and the internal leakage rate were analysed carefully. The results show that energy-based AE parameters, especially the rms value, are more suitable to interpret AE signals generated by internal leakage.     

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.     

气体管道泄漏模态声发射时频定位方法

针对声发射信号频散特性导致基于时延估计的气体管道泄漏定位误差大的问题,提出一种基于模态声发射时频分析的泄漏定位方法。该方法采用平滑伪Wigner-Ville时频分布对两泄漏信号的互相关函数进行时频分析,利用互相关函数的时频谱可同时提取泄漏信号的时间延迟和与之对应的频率;然后根据泄漏声发射信号的主导模态的频散曲线即可确定该频率对应的声速,利用实时确定的声速和时间延迟并根据两传感器之间的距离即可确定泄漏点的位置。实验结果表明,采用时频分析的气体管道泄漏定位误差与互相关相比减少了6倍。所提出的模态声发射时频定位方法能有效抑制泄漏信号的频散,提高泄漏信号的相关性,从而更适合用于声发射管道泄漏定位。     

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。     

载荷作用下管道漏磁内检测信号定量化研究

管道漏磁内检测技术是国际公认的长输油气管道安全维护的最有效方法。为解决载荷作用下长输油气管道漏磁内检测信号定量化问题,本文基于J-A理论建立了改进的三维磁荷数学模型,分析了管道缺陷在内压作用下的磁力学效应对漏磁信号的影响规律,研究了外部载荷作用下缺陷尺寸对漏磁信号影响规律,并通过系统的实验验证了理论模型的正确性。研究结果表明：管道内压增加,材料磁化强度减小,漏磁信号径向分量和轴向分量呈指数函数下降规律;漏磁信号径向分量峰值和轴向分量极大值随着缺陷深度增加而增加,增长率逐渐降低,特征值分别变化6.5%和14.7%;径向分量峰值增长率随长度增加而逐渐降低,轴向极大值线性减小,特征值分别变化21.0%和36.8%,轴向分量对缺陷深度和长度变化更敏感。     

基于小波分析的发动机气门漏气故障诊断研究

对发动机气门漏气声学特性及其振动诊断机理进行了研究 ,在模拟实验的基础上对测取的燃爆段缸盖振动信号进行小波变换 ,比较了三种工况下各尺度小波分解信号能量与总能量之比 ,验证了理论研究的正确性。提取高频带内能量与总能量之比作为特征参数 ,有效地诊断了发动机气门漏气故障。     

环形腔全光纤F-P干涉仪的声发射检测实验研究

提出了一种新型结构的用于声发射检测的全光纤F-P干涉仪。选用2×2光纤耦合器,将耦合器的一个入射端与一个出射端焊接相连,以耦合器代替传统的反射腔面,构成光纤环形传输腔,腔体贴附或埋入待测固体中检测声发射信号。通过理论推导和计算机仿真,确定了此结构光纤传感器的检测特性。实验以大理石板作为待测介质,对利用信号发生器驱动PZT（压电陶瓷）作为已知超声源在大理石板中产生的连续型声发射信号,及冲击波作用下大理石板中产生的突发型声发射信号进行了检测,并利用Fourier变换,得到了声发射信号的特征频率。实验结果表明,此种结构传感器能够检测材料结构中促使光纤轴向伸缩长度的量级为10-8m的声发射信号并识别其特征频率,该结构光纤传感器无需光程的匹配,适用于大尺度构件的监测,为材料结构健康检测与监控提供了一种新的方法。     

基于BP神经网络的管道泄漏声信号识别方法研究

针对城市供水管网泄漏检测需求,进行了泄漏声信号识别方法研究。分析了泄漏信号的时域、频域及波形特点,提取出可用于泄漏信号表征的20种特征参数;基于提取的泄漏声信号特征参数,构建了泄漏声信号BP神经网络识别系统;研究了神经网络结构(隐含节点数、传递函数、学习率)及输入参数的数量和种类对泄漏信号识别效果的影响,并优化出最佳的神经网络结构及输入参数。在以上研究基础上,利用优化后的神经网络对实验室及现场管道泄漏信号进行了交叉训练和识别,结果表明,提出的基于泄漏特征参数的神经网络系统具有较高的可靠性和普适性,可以很好地实现不同场景下泄漏信号的交叉识别,整体识别率达92.5%,为解决不同工况下泄漏信号识别做了有益的探索。     

管式夹套容器内管泄漏声发射监测试验研究

为了监测管式夹套容器在加压过程中的内管泄漏情况,及时发现泄漏源。在实验室内建立管式夹套容器内管泄漏声发射检测系统,通过研究泄漏声发射信号的波形、幅值和不同频带能量分布规律,以及利用波导杆检测管式夹套容器的方法,为现场检测提供试验依据。     

Flow noise characterization of gas–liquid two-phase flow based on acoustic emission

Flow noise of gas–liquid two-phase flow in horizontal pipeline was detected by using the acoustic emission technique (AE); signals were processed by wavelet transform and chaotic analysis. Conclusions were drawn that stratified flow, annular flow and their transition can be divided clearly through multi-scale energy distribution of flow noise, and that dynamic characteristic of flow pattern transition from stratified flow to annular flow, which is described via correlation dimension, acts in accordance with that of annular flow. The dynamic characteristic of the transition condition has already been consistent with that of the annular flow, but due to the low gas flow rate, the energy of the hydrodynamic noise was not enough to reach the complete annular flow pattern. Results were in accordance with experimental facts. Flow noise reflects the complexity of gas–liquid two-phase flow by means of multi-scale energy distribution and chaotic features. Consequently, flow noise based on acoustic emission is a novel and promising point for researching gas–liquid two-phase flow.     

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system.