不同管道特征对声发射信号幅度的衰减影响

分析了引起声发射信号衰减的各种原因,采用美国PAC公司的声发射仪,对一个复杂特征管道中声发射信号的幅度衰减情况进行了试验测量,得出了不同管道特征对声发射信号幅度的衰减影响的结论,并提出相关的建议.     

长输天然气管道阀门内漏在线检测技术试验研究

文中通过搭建管道阀门内漏模拟试验台,采用声发射检测方法,获得不同泄漏量下的声信号特征,根据泄漏所产生的声发射信号的特点,表征参数采用声发射率和能量。结果表明,阀门内漏时声发射信号灵敏度最强处位于阀门附近的管道下游处,假设内漏孔径一定,阀门内漏率仅与阀门压差有关。     

基于网络模型的钢轨裂纹检测方法研究

文章针对钢轨裂纹声发射信号特性人工提取效率低的问题,应用CNN与BiLSTM手段进行钢轨裂纹声发射信号提取的研究,通过预处理采集的实际钢轨裂纹声发射信号,对其特征信息进行提取及分类,分别构建基于一维CNN、二维CNN钢轨裂纹检测模型,证明前者效果优于后者。根据钢轨裂纹信号拥有时序的特性,构建不同递归网络钢轨裂纹检测模型,并对比RNN与LSTM模型检测钢轨裂纹的性能,优化BiLSTM钢轨裂纹检测模型的方法证明能够应用于钢轨裂纹的检测。     

基于小波变换和模态声发射的管道中导波传播特性的试验研究

基于小波变换和模态声发射理论,通过模态声发射试验的方法确定了管道中导波传播的频散特性。通过分析声发射信号的Gabor小波变换幅度在时频空间分布特点,确定某一频率下某一模态导波到达传感器的时间,从而确定该频率下该模态导波的群速度,进而确定其频散曲线。试验确定管道中频散曲线与理论计算曲线较好吻合,证明小波变换是分析频散波时频特性的有效工具。     

冻融作用下玄武岩纤维沥青混合料高温压缩断裂过程声学特性研究

文章通过将声发射技术应用于沥青混合料内部损伤表征,采用高温单轴压缩试验来评价玄武岩纤维沥青混合料的高温抗压性能,并结合声学特性参数对冻融循环处理后的玄武岩纤维沥青混合料试件断裂特征进行识别分析.试验结果表明,声发射信号参数能够很好地反映沥青混合料试件在高温压缩过程中的损伤断裂特征,并且可根据声发射信号的变化将沥青混合料...     

声发射检测技术在管道气压强度试验中的应用研究

声发射检测技术在工艺管道气压强度试验中的应用，能有效避免人员近距离检查存在的气体膨胀爆炸造成的安全风险。文中分析了声发射检测技术工作原理及处理模块主要功能；研究了声发射标准管道压力增长曲线及新安装管线增压要求。通过声发射系统在阿穆尔天然气加工厂某条管线回路中的应用，分析了传感器安装和几何布置、信号数据处理及定位要求。研究了信号衰减、降噪措施及定位方式对声源信号检测准确性的影响，为声发射检测技术在管道气压强度试验中的应用提供借鉴。     

工业管道声发射信号衰减测试及定位分析

简述了工业管道输送流体流动特性,以及声发射技术在管道检测中的应用。采用手持式声发射检测仪器配合不同频率传感器对在役工业管道进行检测,研究焊接有三通、法兰、阀门等外接接口的不规则管道的定位特征,对比分析规则管道与焊有外接接口的不规则管道衰减特征区别,通过对现场数据的分析,得到管道损伤定位与传播声速之间的相互关系以及不同传播速度条件下产生的定位误差,综合分析误差产生原因,作为对现场检测的经验。     

基于声发射技术的管道泄漏检测与定位方法研究

为提高管道无损检测中对小孔径泄漏检测灵敏度、泄漏信号判别准确率以及泄漏点定位精确度,提出一种基于声发射技术并结合信号频域特征提取算法的管道泄漏识别和定位方法。设计管道泄漏检测实验平台进行管道泄漏检测实验,采用经验模态分解方法和小波分解重构相结合的信号处理算法进行降噪并提取信号频域特征,研究不同孔径和不同管道压力下管道泄漏检测和泄漏点定位问题。验证了基于声发射技术并结合信号频域特征提取算法的管道泄漏检测方法的有效性。     

不同温度下花岗岩三轴压缩试验的声发射特性

为研究热-力作用下花岗岩的破裂规律,采用MTS815 Flex Test GT岩石力学试验系统和PCI-Ⅱ声发射仪开展了黑云母花岗岩在不同围压和不同温度作用下的三轴压缩试验。研究结果表明:在20℃,40℃和60℃的温度作用下,声发射事件的密集程度和声发射累计振铃计数以及最大AE能率随着温度的升高而增加,宏观破裂角逐渐减小,岩石的脆性破坏特征逐渐增强;在60℃,90℃和130℃的温度作用下,声发射事件的密集程度和声发射累计振铃计数以及最大AE能率随着温度升高而减小,宏观破裂角逐渐增大,岩石的剪切破坏特征逐渐增强;同时,随着温度升高,剪切破坏造成大量低能量释放率的声发射产生,声发射密集区表现为由小部分能量率很大的声发射和数量较多、低能量释放率的声发射组成。     

基于声发射波形分析的锦屏大理岩岩爆试验研究

岩爆破坏是一种非常复杂的非线性力学行为,影响岩爆的因素众多。文章通过对锦屏大理岩进行两例全程破坏和一例非全程破坏岩爆试验,采集试验过程中的应力和声发射参数,以应力曲线和声发射累计能量曲线为基础确定了关键点,并针对关键点对岩爆过程中的声发射波形进行分析,得到了锦屏大理岩发生岩爆的本质规律。结果表明,锦屏大理岩在单位时间内的声发射波形数分布与关键点有很大相关性,单波占绝大多数,双波、三波和四波在关键点处分布较为集中,多波只在岩爆时刻出现;锦屏大理岩在岩爆阶段与前期加载阶段相比,会有单位时间内波形数数不清的岩爆波产生,是其岩爆阶段的独有特征。     

A note on the use of correlation coefficients for assessing goodness-of-fit in spatial interaction models

The use of correlation coefficients to evaluate the accuracy of spatial interaction models is inappropriate unless such models have been fitted using least squares techniques. In other cases the correlation involves an implicit intercept value and a regression coefficient that may significantly modify the interaction model's estimates. Researchers have not acknowledged the role of these two parameters when the correlation is used. A generalized root mean square error is proposed as an alternative indicator of accuracy that may be used with any model.     

Spectral and time‐frequency analyses of freeway traffic flow

This paper uses spectral and time‐frequency analyses to treat three macroscopic traffic characteristics, namely, time mean speed, volume and occupancy as stochastic processes. Spectral and time‐frequency analyses are performed to characterize power spectral density (PSD), cross‐PSD, autocorrelation and cross‐correlation of these characteristics using TransGuide traffic data collected from four different freeways. It is found that low‐frequency components dominate the PSDs of speed, volume and occupancy at all times. The magnitude of PSDs decreases dramatically as frequency increases and remains almost at a constant level in high‐frequency regimes. A power law is found to exist, which describes the relationship between the frequency and the PSD of traffic characteristics. It is also found that speed can be properly modeled by a narrowband low‐pass stochastic process in a low‐frequency regime and by a nonzero mean white noise in a high‐frequency regime. Strong periodicities and synchronization are both shown in traffic flow. A variety of frequencies can be excited by congestion, and there is no dominant frequency found in stop‐and‐go traffic. Copyright © 2013 John Wiley & Sons, Ltd.     

A continuum model for the dispersion of traffic on two-lane roads

A continuum model for two-lane traffic flow is developed using the theory of kinematic waves in which the wavespeeds in the two lanes are assumed constant but unequal. The transient behaviour is found exactly using Riemann's method of characteristics and an asymptotic model of the long time flow is described. It is shown, that for large times, the traffic concentration moves with a weighted mean wavespeed of the two lanes and disperses about this mean speed as a result of interlane concentration differences generated by the relative wavespeeds. The dispersion can be described by a virtual coefficient of diffusion proportional to the square of the differences of the two wavespeeds and inversely proportional to the rate of lane changing. The technique is extended to describe three-lane traffic flow and to include the dependence of wavespeed upon concentration.     

Characteristics of speed dispersion and its relationship to fundamental traffic flow parameters

Speed dispersion is essential for transportation research but inaccessible to certain sensors that simply record density, mean speed, and/or flow. An alternative is to relate speed dispersion with these available parameters. This paper is compiled from nearly a quarter million observations on an urban freeway and a resulting data-set with two speed dispersion measures and the three fundamental parameters. Data are examined individually by lane and aggregately by direction. The first dispersion measure, coefficient of variation of speed, is found to be exponential with density, negative exponential with mean speed, and two-phase linear to flow. These empirical relationships are proven to be general for a variety of coefficient ranges under the above function forms. The second measure, standard deviation of speed, does not present any simple relationships to the fundamental parameters, and its maximum occurs at around a half to two-thirds of the free flow speed. Speed dispersion may be significantly different by lane.     

Experimental and empirical investigations of traffic flow instability

Traffic instability is an important but undesirable feature of traffic flow. This paper reports our experimental and empirical studies on traffic flow instability. We have carried out a large scale experiment to study the car-following behavior in a 51-car-platoon. The experiment has reproduced the phenomena and confirmed the findings in our previous 25-car-platoon experiment, i.e., standard deviation of vehicle speeds increases in a concave way along the platoon. Based on our experimental results, we argue that traffic speed rather than vehicle spacing (or density) might be a better indicator of traffic instability, because vehicles can have different spacing under the same speed. For these drivers, there exists a critical speed between 30 km/h and 40 km/h, above which the standard deviation of car velocity is almost saturated (flat) along the 51-car-platoon, indicating that the traffic flow is likely to be stable. In contrast, below this critical speed, traffic flow is unstable and can lead to the formation of traffic jams. Traffic data from the Nanjing Airport Highway support the experimental observation of existence of a critical speed. Based on these findings, we propose an alternative mechanism of traffic instability: the competition between stochastic factors and the so-called speed adaptation effect, which can better explain the concave growth of speed standard deviation in traffic flow.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

The value of service reliability

This paper studies the impact of service frequency and reliability on the choice of departure time and the travel cost of transit users. When the user has (α, β, γ) scheduling preferences, we show that the optimal head start decreases with service reliability, as expected. It does not necessarily decrease with service frequency, however. We derive the value of service headway (VoSH) and the value of service reliability (VoSR), which measure the marginal effect on the expected travel cost of a change in the mean and in the standard deviation of headways, respectively. The VoSH and the VoSR complete the value of time and the value of reliability for the economic appraisal of public transit projects by capturing the specific link between headways, waiting times, and congestion. An empirical illustration is provided, which considers two mass transit lines located in the Paris area.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Characterization of driving patterns and development of a driving cycle in a military area

This study develops a gradient-sensitive driving cycle for vehicles in military areas with paved and unpaved roads over steep and undulating terrain. The methodology develops the driving cycle using micro-trips extracted from real-world data taking into account factors that affect fuel consumption. The accuracy of cycle depended on the root mean square error and information value.