基于荷载试验和监测系统的某大跨度悬索桥结构状态评估

为进一步了解大跨度桥梁的结构状态,本文通过对桥梁荷载试验期间的监测系统实时监测数据、现场试验测量数据和大桥有限元模型模拟计算数据的对比挖掘分析,以定量化的形式通过与结构状态相关的参数指标,评估桥梁的结构状态。以国内某新建大跨悬索桥为例,通过安装的健康监测系统采集桥梁在静载试验条件下各控制截面的挠度、应变、振动等结构响应实时监测数据,计算桥梁挠度和应变特征值,采用频谱分析等方法计算大桥的模态参数,然后基于挠度、应变、模态参数的监测结果与现场试验测量结果、有限元模型计算结果的对比分析,并参照现场荷载试验评定方法,评估桥梁的结构状态。实验结果表明:监测系统时程数据可观测到明显的加载和卸载情况,监测系统运行良好,在试验荷载下桥梁处于弹性工作状态,整理受力状态良好,大桥结构整体刚度满足设计荷载的正常使用要求。作为新建桥梁,该评估结果还可作为桥梁的初始状态,作为后续评估桥梁结构状态和健康监测系统工作状况的参考基准。     

蔡家嘉陵江特大桥长期健康监测系统的设计及实施

基于蔡家嘉陵江特大桥工程基础,结合现代化的传感技术和自动远程监控技术,提出基于结构健康评价模型的桥梁长期健康监测系统,研究其各个子模块的集成方案及协同工作。该系统对桥梁结构状态进行长期实时监测与评估,能够在恶劣天气、混乱交通及出现紊乱的运营状态时及时地发出预警信号。通过该系统在蔡家嘉陵江特大桥中的运行实践证明:该系统功能完备、可靠性高,可为桥梁维护、维修与管理决策提供依据。     

桥梁健康监测系统在线结构分析及状态评估方法

为实时在线评估桥梁结构实测荷载与响应状态的相关性,提出桥梁健康监测系统的在线结构分析及状态评估方法。编制有限元分析程序内核,在程序内核中内嵌桥梁结构有限元模型,通过授权的数据库接口,读取健康监测系统中的实时荷载数据,对荷载数据进行统计分析,然后计算结构的理论响应值,并与健康监测系统中的结构实测响应值进行对比,对结构行为进行评估。某桥主桥为主跨460m的五跨连续双塔双索面半飘浮体系斜拉桥,应用该方法对该桥进行在线分析和状态评估,评估结果显示,根据实测荷载分析得到的结构响应和实测结构响应吻合较好,结构荷载和响应具有线性相关性,结构运营正常。     

桥梁维护与管理系统研究

介绍了桥梁管理与维护系统的基本结构 ,分析了状态监测、状态评价及数据库建立等所涉及的若干问题 ;在状态监测中 ,对监测周期与部位以及检测方法的选择进行了介绍 ;在状态评价中 ,简要介绍了基于神经网络的评价系统和专家系统评价方法。     

汽车ABS中的模糊神经网络模型参考自适应控制策略

针对汽车制动的特点以及汽车防抱死制动系统的性能要求,建立了汽车的数学模型,提出一种模糊神经网络的自适应控制方案,构建了基于模糊神经网络的控制器和辨识器的结构模型。通过对网络参数的离线训练得出其初值,在控制过程中对网络参数进行在线微调,实现对汽车制动过程的有效控制。仿真结果表明:在不同的路面,汽车均能保持在最佳滑移率附近进行制动,制动时间及距离比较理想,满足ABS的安全性能要求。     

刚构——连续组合梁桥监测监控技术研究

以新疆伊犁特大桥施工监测监控为工程背景,对预应力混凝土刚构连续组合梁桥的施工监测监控的方法、施工监测监控系统的建立以及施工监控的实施步骤进行了探讨。桥梁在结构设计时的参数选取、施工状况的确定和结构分析模型等诸多因素的影响,使得桥梁施工过程中结构的实际状态与设计状态难以吻合,通过监控实施及监测结果与理论计算值的对比分析,保证桥梁建成后的线形和安全。     

基于BP神经网络的桥梁技术状态评估

用养护规范中17个评价指标作为输入层网络神经元,把桥梁损伤等级参数作为输出层神经元,建立了桥梁评估3层BP神经网络模型.选用湖北省110座旧桥的评估数据作为训练样本,后10个作为测试样本,经过2 068次迭代运算的网络训练,得到了误差满足精度要求的收敛网络.将待评估的桥梁参数输入训练好的网络,得到评估桥梁的技术状态等级...     

洞庭湖大桥结构状态在线监测系统

桥梁结构状态在线监测系统是目前桥梁工程领域的研究热点,其目的是为结构损伤诊断、可靠性评估和维护提供科学的依据和指导。在分析了传统人工检测的不足的基础上,以洞庭湖大桥为例,从适用性和经济性角度,讨论了适于桥梁状态在线监测的监测项目和测量手段,并分析了监测系统结构、网络拓扑结构和通讯技术等关键问题,同时针对现有桥梁结构损伤诊断和评估的不足,提出建立因特网远程监测与分析平台,将异地专家与现场有机联系起来。     

基于统计及形态特征指标的桥梁线形监测评估

为探讨大跨度桥梁线形实时在线监测及安全评估方法,以武汉阳逻长江公路大桥为工程背景,建立了液压差半封闭式连通管线形监测系统,介绍该桥健康监测系统中线形监测的测点布置、采集方案策略,重点研究了以统计及形态特征为指标的桥梁线形监测加权评估方法。研究表明:该系统能够实时在线监测并评定桥梁线形变化特征,且测试精度高、长期稳定性好;统计及形态特征指标集单个测点与全部测点数据评估为一体,可有效运用于大跨度桥梁线形监测的状态评估。     

基于频率响应的发动机混合气控制系统在线参数辨识方法

混合气空燃比控制是汽油车的基本排放控制策略。氧传感器特性等系统参数的变化会对混合气的闭环控制效果产生影响。文章介绍了基于频率响应的发动机混合气控制系统在线参数辨识方法,辨识过程不会对排放和驾驶性带来负面影响;对氧传感器和系统参数的自适应和学习,能够提高混合气控制精度,有利于降低排放。基于频率的参数辨识方法可以推广应用于具有一阶惯性延迟环节特征的物理系统。     

危化品运输设备状态实时监测系统开发

提出了一种危化品运输设备状态实时监测系统的设计和实现方法,阐述了系统的总体设计思想,介绍了系统的工作方式,开发的系统实现了危化品运输设备全过程的监测,提高了危化品运输的安全性、可靠性,为现代危化品物流提供了一种安全解决方案.     

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.     

汽车车架OMA实验模态分析

模态参数辨识是系统辨识的一部分,通过模态参数的辨识,可以了解系统或结构的动力学特性,这些动力特性可作为结构有限元模型修正、故障诊断、结构实时监测的评定标准和基础。本文以212车架为研究对象,综合考虑悬挂条件,激励方式及布点位置对实验结果的影响,对其进行模态试验。通过使用ICATS软件对采集到的数据进行时域内的工作模态分析,然后得到模态参数。     

A traffic monitoring stream-based real-time vehicular offence detection approach

Traffic offences are becoming increasingly serious as traffic volume increases rapidly in large cities, causing serious property damage and threatening public safety. Existing traffic monitoring systems lack the capability of detecting various types of offences in real-time. This paper proposes a novel monitoring stream-based vehicular offence detection algorithm, which discovers various types of offences from high-throughput traffic monitoring stream in real-time. An offence detecting and monitoring system is also designed and implemented. In order to achieve real-time detection, parallel computing techniques are utilized. An optimized data structure, a one producer-multiple consumer model and a re-hash strategy are proposed to reduce the synchronization cost incurred by multiple threads in the parallel implementation. Both real-world data and synthetic data are applied in the experiments. Experimental results demonstrate that the proposed algorithm is able to discover three types of offences from high-throughput traffic monitoring stream in real-time. Scalability is also observed. The experimental results indicate that the proposed system is sufficiently efficient to provide real-time offence detection for major metropolises.     

Employment of instrumented vehicles to identify real-time snowy weather conditions on freeways using supervised machine learning techniques – A naturalistic driving study

This study introduces the idea of using vehicles as weather sensors to identify real-time weather on freeways in the same context as Road Weather Information System (RWIS) but in a continuous, trajectory-level, and for road segments allocated in the vehicles paths. The study developed a novel approach to detect snowy and clear weather conditions by utilizing real-time data collected from vehicles' external sensors and CANbus. The proposed approach used time series datasets from the SHRP2 Naturalistic Driving Study (NDS), collected during normal driving conditions on freeways. Trips occurring in snowy weather alongside matched trips in clear weather were segmented into time- and distance-based segments such as a one-minute, one-mile, and half a mile. Three assemblies of the input data are considered in the modeling step: data collected from external sensors, CANbus data, and these two data combined. Data analysis was implemented using the Deep Learning Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees models. The results indicate that using different segmentation levels provides decent results in detecting snowy weather. The accuracy in estimating the real-time snowy weather was in ranges of 80% to 85%, 71% to 79%, and 73% to 83% for the one-minute, one-mile, and half mile segmentation types, respectively. The GBT model performed the best among all models based on the area under the Receiver Operating Characteristics (ROC) curve, the highest cumulative percentage in estimating the snowy weather using the lower portion of the population, and the highest overall accuracy. Results indicated that an accuracy of 83% in estimating snowy weather conditions could be accomplished using the data collected from external sensors only without accessing CANbus data.     

基于随机加权自适应容积卡尔曼的电池SOC估计

准确估计锂离子电池荷电状态（SOC）对于突破电动汽车发展瓶颈，推动电动汽车商业化至关重要。针对动力电池模型参数辨识问题，提出基于遗忘因子的递推最小二乘法（FRLS）的模型参数在线识别方法。实时测量动力电池电流和电压数据，在线辨识模型参数并实时更新，实时反映电池内部参数的变化过程，对电池动态特性进行实时模拟。针对容积卡尔曼（CKF）滤波过程中对噪声敏感的问题，提出一种基于随机加权思想的自适应容积卡尔曼滤波（ARWCKF）方法。相比于常规CKF容积点权值始终不变，通过引入随机加权因子，自适应调整容积点权值并对系统噪声、状态向量及观测向量进行预测，抑制系统噪声对状态估计的干扰，避免因容积点权重值固定所带来的误差。针对CKF算法在容积点计算过程中由于状态方差矩阵失去正定性导致的平方根分解无法使用的问题，提出基于奇异值分解的容积点计算方法，克服由于先验协方差矩阵负定性变化而导致的滤波精度下降等问题，并进行多种工况、温度下不同SOC初值的对比验证。结果表明：所提出的基于遗忘因子的递推最小二乘法的在线参数辨识及ARWCKF滤波方法具备良好的估计精度及收敛能力，最大电压估计误差不超过40 mV，SOC估计误差不超过1%。     

大直径泥水盾构常压刀盘温度在线监测系统设计与应用———以杭州望江路过江隧道工程为例

在盾构施工过程中,刀盘温度的异常升高会导致刀盘磨损加剧,甚至造成刀盘变形,严重影响盾构施工及安全。为了实时监 测盾构刀盘温度,避免刀盘温度过高所带来的危害,以杭州市望江路过江隧道工程为依托,建立一套大直径泥水盾构常压刀盘温度 在线监测系统,结合无线传输技术,利用安装在刀盘背面的传感器收集刀盘温度数据,并对数据进行整理和分析。结果表明: 1)该 系统能避免信号屏蔽和泥水盾构掘进的干扰,长时间稳定监测刀盘温度; 2)盾构单环掘进时,刀盘温度呈周期性变化,与盾构工作 流程相匹配; 3)盾构连续掘进时,掘进产生的热量会在刀盘上稳定积累,使得刀盘温度曲线的峰值持续升高; 4)在刀盘温度异常升 高时,通过向刀盘注入分散剂,并观察刀盘温度变化,可以对刀盘形成泥饼和前方地质突变2 种情况进行区分。本文设计的刀盘温 度在线监测系统可以用于分析刀盘情况、判断地质变化和调节掘进参数。     

Adaptive feedforward control of a steer-by-wire system by online parameter estimator

The tracking control of the steer-by-wire (SBW) system to achevie desired steering motion is the core issue for the design of algorithm. Most of model-based tracking control assumed the constant parameters without the consideration of dynamic characteristics. The external disturbances and model nonlinearities can bring uncertainties of the system parameters. To reduce the influence of parameter uncertainties, an online estimator by output error identification method is proposed to estimate the dynamic parameters of a SBW system. Meanwhile, the parameter gradient projection method is applied to eliminate the parameter drift, while a full order state observer is developed to weaken the effects of noise disturbance during the parameter identification. Since the sensitivity of parameter uncertainties for the feedforward control, the online estimator is incorporated into the control model and improve the controlled robustness. The proposed adaptive feedforward controller is conducted by the real-time experiments to show the tracking performance.     

智慧水务在市政排水系统效能提升中的应用

以新塘镇排水系统为例，结合广州市建设国家新型智慧城市的战略目标以及实现新塘镇市政排水系统智慧化建设的需求，介绍了智慧水务在污水管网系统“挤外水、提浓度”以及在雨水系统防洪排涝预测中的应用。对污水管网系统管道水位、水质、流量和外水入侵情况进行在线实时监测，对提升污水厂运行效能有较大裨益，对雨水系统易涝点和外江水位进行实时监测和预警，构建城市内涝风险预警体系，避免灾害发生，从而实现排水管网系统的运行和管理智能化、专业化和精细化。