A real-time impact detection and diagnosis system of catenary using measured strains by fibre Bragg grating sensors

This paper describes an impact detection system using strain signals based on fibre optic sensors(FBG) for the real-time monitoring of the catenary system. The proposed detection system consists of three subsystems: a measuring system, a data processing and analysis system, and a status display and data access system. Because the strain signals obey the normal distribution, to monitor the catenary system in real time, a novel method that combines mobile standard deviation with the mobile Pauta criterion is proposed to distinguish real impact from the strain signal background. The use of this adaptive judging method reduces the misjudgment rate of impacts and improves the impact recognition accuracy. These impacts can be identified by the data analysis system, which provides impact location and their causes using the features of the catenary system. This method can simplify the detection system compared with the traditional location method. An application to a commercial metro line system indicated that the impacts on the catenary system were main caused by overlaps, expansion joints or steady arms, and were verified by correspondence with the floor plan of the catenary and manual inspection results. These results verified the reliability and effectiveness of the proposed impact detection system.     

Development of a new time domain-based algorithm for train detection and axle counting

This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

VIV response of a flexible cylinder with varied coverage by buoyancy elements and helical strakes

Many significant engineering challenges have emerged as the petroleum industry has moved their field development and production activities into increasingly deeper water depths. The design of deepwater marine risers presents the combined challenges to minimize top tensioning requirements, mitigate any flow-induced vibrations, and if possible to increase the expected fatigue life of these slender structural members. As part of the design process to achieve these goals external buoyancy modules and strakes have been employed. To gain insight into the complex multi-mode response behavior a recent experimental study was performed and the analysis of selected data sets is presented. In the experiments a horizontal cylinder with a length to diameter ratio of 263 was fitted with a variety of strake and buoyancy element configurations. The models were towed at uniform speeds ranging from 0.4 to 2.0 m/s and fiber optic strain gages were used to measure both in-line and cross-flow strain response. The resulting time series information was processed utilizing the method of time domain decomposition formulated for strain data input and the introduction of modal assurance criterion to resolve the modal strain information that included frequency, mode shape, and critical damping ratio information. The pre-tensioned cylinder without appendages was used as a base case and the results were basically consistent with expectations. In the case of 0.8 m/s low-tension test, multiple closely spaced non-overlapping peaks were observed in both in-line and cross-flow directions and were identified as being of the same mode with mode shapes distorted away from purely sinusoidal behavior. The test data for the 100% coverage by helical stakes demonstrated the effectiveness of that suppression device over the range of current velocities investigated. The most interesting case was that of a staggered combination of helical strakes and buoyancy element whose total for each type of coverage was equal. This effective asymmetric VIV suppression approach is presented and discussed in detail.     

基于FGU的船体变形测量技术中时间延迟补偿方法研究

基于光纤惯性测量单元(Fiber Gyro Unit,简称FGU)的角速率匹配法是船体变形测量技术的发展方向,但是在实际应用中两套测量单元之间存在时间延迟,会影响船体变形测量精度。该文分析了时间延迟的产生机理以及时间延迟对变形估计的影响。并基于二阶马尔科夫模型对船体变形建模,建立卡尔曼滤波状态方程和量测方程,提出了一种时间延迟补偿方法,将时间延迟扩充为状态量,对其进行实时的估计和补偿。最后通过仿真并结合实船试验数据,对比分析了时间延迟补偿前后的变形估计效果,验证了此方法的有效性。     

基于光纤光栅传感技术的江阴大桥结构应变监测研究

江阴大桥自1999年通车以来,就一直经历着大交通和重载交通的双重考验.主桥钢箱梁受交通荷载的影响最为直接,极易因荷载的不断作用而产生疲劳损伤.对荷载影响反应最直接的就是主桥钢箱梁结构的内部应变,通过对结构应变的监测,可以较好地掌握桥梁受动态荷载作用下的结构响应.本文采用光纤光栅传感技术对钢箱梁结构的重点部位进行应变监测...     

光纤光栅传感测量中交叉敏感问题及解决方案

目前已有的基于光栅Bragg光栅的各种传感器,基本上都是直接或间接的利用应变和温度改变光纤的中心波长,以达到测试所需被测量的目的。但是中心波长不仅会随测试点的应变发生改变,还会随着环境温度的波动而发生变化,即存在温度和应变交叉敏感的问题。解决温度、应变交叉敏感问题即实现温度和应变的分离检测是光纤Bragg光栅应变传感器得到实际应用的重要前提[1]。文章从理论上分析了引起交叉敏感的物理机制,较为全面地介绍了几种主要的解决方案,并对其主要特点进行了简单的分析。     

A mixed-integer linear program for optimizing sensor locations along freeway corridors

How to optimally allocate limited freeway sensor resources is of great interest to transportation engineers. In this paper, we focus on the optimal allocation of point sensors, such as loop detectors, to minimize performance measurement errors. Although it has been shown that the minimization problem can be intuitively formulated as a nonlinear program, the formulation is so complex that only heuristic approaches can be used to solve the problem. In this paper, we transform the nonlinear program into an equivalent mixed-integer linear model. The linearized model is shown to have a graphical interpretation and can be solved using resource constrained shortest path algorithms. A customized Branch-and-Bound technique is then proposed to solve the resource constrained shortest path problem. Numerical experiments along an urban freeway corridor demonstrate that this sensor location model is successful in allocating loop detectors to improve the accuracy of travel time estimation.     

基于FBG传感器的船体结构实时监测系统

为实时监测船舶在高速航行时船体结构的状态,评估船体关键部位的强度,提高船舶的安全性.基于FBG传感技术,结合船体结构特征,采用信号处理和强度评估等理论,构建船体结构强度监测体系,以实现船体结构典型位置技术状态监测及预警判断.研究成果可为各种类型船舶的安全健康监测提供一定的借鉴和参考.