DENOISING AND HARMONIC DETECTION USING NONORTHOGONAL WAVELET PACKETS IN INDUSTRIAL APPLICATIONS

New industrial applications call for new methods and new ideas in signal analysis. Wavelet packets are new tools in industrial applications and they have just recently appeared in projects and patents. In training neural networks, for the sake of dimensionality and of ratio of time, compact information is needed. This paper deals with simultaneous noise suppression and signal compression of quasi-harmonic signals. A quasi-harmonic signal is a signal with one dominant harmonic and some more sub harmonics in superposition. Such signals often occur in rail vehicle systems, in which noisy signals are present. Typically, they are signals which come from rail overhead power lines and are generated by intermodulation phenomena and radio interferences. An important task is to monitor and recognize them. This paper proposes an algorithm to differentiate discrete signals from their noisy observations using a library of nonorthonormal bases. The algorithm combines the shrinkage technique and techniques in regression analysis using Shannon Entropy function and Cross Entropy function to select the best discernable bases. Cosine and sine wavelet bases in wavelet packets are used. The algorithm is totally general and can be used in many industrial applications. The effectiveness of the proposed method consists of using as few as possible samples of the measured signal and in the meantime highlighting the difference between the noise and the desired signal. The problem is a difficult one, but well posed. In fact, compression reduces the level of the measured noise and undesired signals but introduces the well known compression noise. The goal is to extract a coherent signal from the measured signal which will be “well represented” by suitable waveforms and a noisy signal or incoherent signal which cannot be “compressed well” by the waveforms. Recursive residual iterations with cosine and sine bases allow the extraction of elements of the required signal and the noise. The algorithm that has been developed is utilized as a filter to extract features for training neural networks. It is currently integrated in the inferential modelling platform of the unit for Advanced Control and Simulation Solutions within ABB’s industry division. An application using real measured data from an electrical railway line is presented to illustrate and analyze the effectiveness of the proposed method. Another industrial application in fault detection, in which coherent and incoherent signals are univocally visible, is also shown.     

新型的长钢轨吊运装置

本文主要通过介绍 CGD 型长钢轨吊运装置的结构和工作原理,阐述解决长钢轨吊运中准确定位、同步运行、自动夹轨和缓冲防摆等难题的先进技术.     

Research on Planning & Evaluation for Track Traffic Under China''''s Sustainable Development Strategy

1Preface　Economic ,social,environmentalandotherproblemswerediscussedin《OurCommonFuture》[1]byWorldEnvironmentandDevelopmentCommissionandtheconceptofsustainabledevelopmentwasputforward .Thenthesustainabledevelop mentbecomesthehotspotofallovertheworldandthenewdevelopmentmodeisattractingmuchattention .Theconceptofdevelopmentisextendedfromonlyeconomyincreasetoanewstatusinwhicheconomy ,societyandresourceenviron mentwillharmoniouslydevelop .　Tracktrafficplaysakeyroleinsustainabletransportation…     

城市轨道交通高峰时段站间起讫点矩阵预测模型

高峰小时单向最大断面客流量是城市轨道交通规划与设计阶段的重要参考依据.为了确定这一参数,需对高峰时段内出发的乘客选择的出发时刻与路径进行预测.高峰时段站间起讫点(OD)矩阵反映了城市轨道交通乘客的出行需求,是整个预测的基础.在全天站间OD矩阵已知的前提下,以中国重庆市为研究对象,首先分析传统的重力模型在预测城市轨道交通高峰时段站间OD矩阵时的优、缺点,并在此基础上进一步提出站间客流高峰时段系数模型.比较结果表明,在同一数据源下,站间客流高峰时段系数模型能有效改善传统的重力模型所存在的缺陷,预测结果明显更优.该模型预测结果的标准误差为12.90人次,相较于重力模型的29.33人次降低了56.02%.     

重载钢轨磨耗预测模型及接触斑网格密度研究

基于车辆-轨道耦合动力学及轮轨滚动接触分析,结合材料磨损理论建立了钢轨磨耗预测模型,编制了计算程序,可实现钢轨磨耗具体分布及发展的定量预测分析.鉴于轮轨接触斑离散化网格密度在预测模型中的显著影响作用,从接触力、磨耗分布等方面对这一因素的影响机理进行分析,探讨合理的接触斑网格密度.研究结果表明:网格密度不影响计算结果的正确性,但是稀疏的网格密度得到的蠕滑力及磨耗分布存在较多尖锐形状突变,增大密度可提高精度及磨耗分布平滑性,但会成倍增加计算代价;网格密度20×20时,钢轨磨耗速率变化由剧烈趋于稳定,继续增大密度改善效果已不明显,建议预测模型中接触斑网格密度取20×20,在确保精度的同时尽可能拥有较高计算效率.     

上海郊区轨道站客流与站域出行活力匹配性研究

依据手机信令数据构建站域活力指标对站域人群出行强度与范围进行测度,针对站域活力与站点客流量的匹配差异性,利用象限法将上海郊区77个轨道站分为3类,即高活力高客流、高活力低客流、低活力低客流,分别占比38%、18%、44%。结合站域开发强度、轨道自身性质等多维属性构建多分类Logit模型,结果表明,相对于高活力高客流站点,高活力低客流站点主要分布于未直接穿越城市主城区的线路上,更需要提高轨道竞争力及地面公交与轨道的接驳水平、降低站域人群对小汽车的依赖度来提高客流;低活力低客流站点,主要分布于开发密度低的远郊,加强站域开发水平是提高站域活力与客流的关键。研究结果为超大和特大城市郊区轨道站点客流与站域活力两者协同发展机理提供参考。     

钢轨非接触式无损检测技术数值模拟研究

文中针对现有钢轨检测技术不足的问题,提出了基于空气耦合导波的钢轨非接触式无损检测方法,建立了可模拟空气耦合导波激励与接收全过程的声固耦合仿真模型,并基于声学理论对仿真模型进行了验证。首先,通过该数值模型模拟分析了轨底不同损伤程度对接收导波信号的影响;然后,考虑空气耦合导波受高强度随机白噪声的影响,提出了基于激励信号中心频率小波系数的损伤评估方法。结果表明：根据Snell定律及声学理论,钢轨空气耦合导波检测的最优激励角与接收角为6.6°;钢轨空气耦合接收导波具有波形稳定、能量集中及抗干扰能力强的特点;无论损伤程度大小及所处空间位置如何,接收到声压时域信号波包中心到达时间均基本一致;不同损伤程度对应损伤指数范围有明显差异;基于激励信号中心频率的小波系数法简单可行,准确度高,适用于已知窄带导波信号的损伤信息提取,在接收信号受噪声污染严重的情况下仍能够有效识别出钢轨损伤;基于空气耦合导波进行钢轨损伤识别具有可行性。     

海州露天煤矿滑坡综合整治

本文介绍海州露天煤矿非工作帮一次巨大滑坡的整治工程实践,结合岩体的完整程度、滑坡的推力大小、以及软弱岩层等特点,确定以抗滑桩为主,疏干排水措施为辅的综合整治方案,文中论述了以工程造价最低为目标函数,对大型钢筋混凝土抗滑桩的主要设计参数进行优化的方法,由于综合整治,获得了明显的技术和经济效果.     

Mn-V-Nb重轨钢组织与性能的研究

以研制特级耐磨重轨钢为目的,在 U 74普碳耐磨重轨钢化学成分的基础上,加入微量V,Nb 元素并适当提高 C,Mn 元素的含量,设计并冶炼出 Mn-V-Nb 重轨钢。研究了该钢的组织及性能。结果表明:Mn-V-Nb 钢的常规力学性能达到了特级耐磨轨钢的要求。在实验室条件下,抗磨损性能和抗接触疲劳性能分别较 U74 钢提高3.7 倍和 4.6倍。提高的原因在于珠光体片间距的细化及 V,Nb 元素的沉淀强化作用。     

浅谈轨道交通特色资源的数字化建设

论述了轨道交通特色馆藏资源开发的必要性、意义及轨道交通特色资源建设的主要内容和实现方式,指出了图书馆文献资源数字化建设的发展方向。