Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

Research on cubic polynomial acceleration and deceleration control model for high speed NC machining

To satisfy the need of high speed NC (numerical control) machining, an acceleration and deceleration (acc/dec) control model is proposed, and the speed curve is also constructed by the cubic polynomial. The proposed control model provides continuity of acceleration, which avoids the intense vibration in high speed NC machining. Based on the discrete characteristic of the data sampling interpolation, the acc/dec control discrete mathematical model is also set up and the discrete expression of the theoretical deceleration length is obtained furthermore. Aiming at the question of hardly predetermining the deceleration point in acc/dec control before interpolation, the adaptive acc/dec control algorithm is deduced from the expressions of the theoretical deceleration length. The experimental result proves that the acc/dec control model has the characteristic of easy implementation, stable movement and low impact. The model has been applied in multi-axes high speed micro fabrication machining successfully.

Novel Rotors with Low Eddy Current Loss for High Speed Permanent Magnet Machines

Due to the large rotor eddy current loss and low thermal conductivity of carbon fiber sleeve,the high temperature usually occurs in high speed permanent magnet machines(HSPMMs)at the rated operation condition,resulting in irreversible demagnetization of the permanent magnet(PM).To obtain low rotor temperature,two novel rotor structures with low rotor eddy current loss are proposed in this paper.With the output torque and air gap flux density unchanged,the performance of HSPMMs with the two proposed rotor structures are analyzed based on finite element algorithm(FEA),including eddy current loss and temperature.Finally,the appropriate parameters of the proposed rotor structures are selected,and the electromagnetic(EM)performance,rotor stress and temperature are compared with those of the conventional rotor structure.Index Terms-Eddy current loss,finite element algorithm(FEA),electromagnetic(EM)performance,high speed permanent magnet machines(HSPMMs).

Design and Implementation of Dynamic High-Speed Switches in Super Base Station Architectures

Novel centralized base station architectures integrating computation and communication functionalities have become important for the development of future mobile communication networks.Therefore,the development of dynamic high-speed interconnections between baseband units(BBUs)and remote radio heads(RRHs)is vital in centralized base station design.Herein,dynamic high-speed switches(HSSs)connecting BBUs and RRHs were designed for a centralized base station architecture.We analyzed the characteristics of actual traffic and introduced a switch traffic model suitable for the super base station architecture.Then,we proposed a data-priority-aware(DPA)scheduling algorithm based on the traffic model.Lastly,we developed the dynamic HSS model based on the OPNET platform and the prototype based on FPGA.Our results show that the DPA achieves close to 100%throughput with lower latency and provides better run-time complexity than iOCF and HE-iSLIP,thereby demonstrating that the proposed switch system can be adopted in centralized base station architectures.

An Improved Method of Retrieving Sea Surface Wind Speed Based on a Four-Layer Medium Model at High Sea States

Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.

高速铁路动静态轨检数据里程对齐与误差修正

轨道几何动、静检测数据间的精确匹配对探明高速铁路线路服役状态和制定准确可靠的养护维修策略具有关键作用。针对动静里程匹配算法研究较少的现状,提出利用动、静态实测数据波形匹配,建立基于互相关函数与动态时间规划相结合的两阶段修正算法,并以距离误差作为评价指标。结合某高铁线路轨道几何检测数据的案例分析,以静态检测数据作为参考基准,对动态检测数据进行里程误差评估与修正。结果表明,两阶段算法修正效果显著,累积距离误差降幅超过93%,修正后的动、静态检测数据严格对齐保证了动态检测数据的可靠性与有效性。

基于边缘计算的高速光纤通信链路时延估计研究

由于未对接收信号进行去噪处理,导致光纤通信链路时延估计存在估计误差大、准确率低的问题,为了解决方法中存在的问题,提出基于边缘计算的高速光纤通信链路时延估计方法。通过边缘计算设计了高速光纤通信网络架构,基于网络架构中的边缘计算层,通过直射链路的方式构建高速光纤通信链路模型,接收光信号,并使用广义相关算法对接收到的信号进行去噪处理,根据去噪处理结果,结合卡尔曼滤波方法与Round-trip算法估计信号时延。实验结果表明,所提方法的高速光纤通信链路时延估计准确率高、效果好,具有较高的实际应用价值。

基于IBES-XGBoost的高速铁路沿线风速预测模型

为保证高速铁路沿线风灾预警信息具有较高时效性,需要进行高速铁路沿线超短期风速的提前多步预测。针对众多预测模型在预测中可能存在较大误差的问题,采用Tent混沌映射和BFGS拟牛顿法对秃鹰搜索算法进行改进,并用改进的秃鹰搜索算法(IBES)对XGBoost的初始参数进行优化。在构建IBES-XGBoost模型时,加入风速以外的其他气象特征,以提高预测精度。实验结果表明:(1)改进的秃鹰算法相比其他智能优化算法有更好的寻优能力,与其他模型相比IBES-XGBoost在超短期风速的提前多步预测上有着较高的精度和较好的拟合效果。(2)Tent混沌映射和BFGS拟牛顿法对秃鹰算法有着较好的改进效果。(3)IBES-XGBoost能为高速铁路规范下的大风预警提供可靠的提前多步预测结果。

基于径向基函数神经网络算法的高频转阀阀芯稳定性

针对伺服电机驱动高频转阀时受液动力矩变化影响造成高频输出精度下降的问题,以液压马达作为动力源,提出一种基于径向基函数神经网络算法的转阀阀芯转速控制策略。首先,搭建高频转阀阀芯转速控制系统的数学模型;其次根据数学模型在MATLAB/Simulink平台搭建仿真模型,对不同算法作用下阀芯转速控制特性进行仿真分析;最后建立高频转阀转速控制系统实验台,对不同算法作用下阀芯转速控制特性进行实验研究和理论验证。结果表明:与常规PID控制方法相比,基于径向基函数神经网络的高频转阀转速控制策略转速控制系统阶跃响应所需调整时间最少为0.16 s,超调量小;三角波与正弦波转速跟踪误差均值下降最大值分别为46.51%、53.69%;6 MPa、10 MPa下,转速稳态误差均值分别下降34.92%、38.26%。径向基函数神经网络算法有效提高了高频转阀阀芯转速控制精度。

自动确定边角混合网独立闭合差的增量算法

为评定外业观测数据质量和发现粗差,提出了一种闭合差搜索的通用算法。首先,针对高速铁路隧道中的各类边角混合网,基于平差理论,计算由边角混合网中测站数、非测站数和观测数确定其独立闭合差的数目,给出最简闭合差构成原则,求出余弦和正弦闭合差,推导闭合差限差计算模型,建立由闭合差探测观测值粗差的方法;进而导出闭合差增量与测站递增产生的重叠观测点数之间的数学关系式,设计数据结构实现测站与结点间双向多对多的拓扑映射关系,并基于分治思想,提出队列-测站双循环的增量算法来自动搜索最简且独立的闭合差;最后,以西南某高速铁路隧道构建的边角混合网为例进行算法验证。结果表明:增量算法能够在混合网中自动确定100%数量的最简独立闭合差,对观测值中存在的粗差100%予以探测,验证了采用该算法搜索闭合差及检核粗差的有效性。

高速铁路多列车客票预分与通售策略协同优化

在客票预分方案中设置通售票能使分配票额有效适应售票过程中动态变化的购票需求,但也容易出现通售票过早肢解,从而导致后续长途OD对旅客购票失败、通售票无法充分利用。为避免此情况出现以进一步提高通售票的利用率,考虑通售票销售策略与铁路客票预分方案的协同优化问题,实现由OD固定票、通售票构成的客票预分方案与通售票发售策略(以下简称“通售策略”)的协同优化。首先,基于旅客随机购票需求描述、旅客购票列车选择分析,构建铁路客票预分方案与通售策略协同优化模型,以客票总收益最大化为目标,考虑列车席位能力、列车服务、客票量上下限以及通售发售策略等约束;其次,在生成多套购票需求样本的基础上,结合旅客出行购票过程的仿真,设计模型求解的粒子群算法。算例结果表明对比不考虑通售策略的客票预分方案,本文优化方法能使列车平均客座率提高4.96%、列车客票总收益提高2.74%,充分验证了引入通售策略能有效实现客票的灵活与充分利用。

基于Adaline算法的高速磁浮列车谐波检测及抑制方法

[目的]为了减小高速磁浮谐波对牵引网产生的影响,保障高速磁浮列车的正常运行,需研究高速磁浮牵引供电系统中的谐波特性及其相应的谐波抑制方法。[方法]分析了高速磁浮牵引供电系统的构成,并对双三相半控12脉冲多重整流电路及其谐波进行了理论分析和仿真。根据Adaline神经网络算法原理和高速磁浮谐波特性,构建基于Adaline的高速磁浮谐波检测模型。采用谐波补偿控制策略,产生与检测出的谐波方向相反、大小相等的电流作为补偿信号,对系统畸变电流进行谐波抑制。[结果及结论]仿真结果表明:采用基于Adaline算法的谐波检测及抑制方法前,谐波畸变率为11.16%,而采用该方法后,各次谐波含量明显降低,谐波畸变率下降为1.41%。研究结果表明:所提谐波检测及抑制方法可以有效减少谐波畸变,提高牵引供电系统的稳定性和工作效率。

基于改进生物地理学算法的列车ATO多目标优化研究

高速列车运行过程优化是一个多目标、非线性优化问题.为研究列车自动驾驶系统(automatic train operation,ATO)多目标优化问题,以列车运行的准时性、停车精确性、舒适性、能耗性为控制目标,列车动力学模型为约束条件,同时考虑列车惰行过分相区,建立列车多目标优化模型,提出了一种改进的生物地理学(biogeography-based optimization,BBO)优化ATO速度曲线方法.为提高算法优化性能,使用更加倾向自然法则的双曲正切变体迁移模型;在变异过程中使用差分进化(differential evolutionary,DE)变异策略,提高种群多样性,同时加入柯西分布随机数帮助算法跳出局部最优;利用反向学习提高变异后个体的多样性,保证算法的全域搜索.同时通过基准测试函数验证该算法收敛速度和全局优化能力的优越性.以CRH3型高速列车和汉宜客运某线路进行仿真实验,结果表明,所提方法可以使列车追踪运行更加高效、舒适、安全和节能,其中舒适度提升了39.24%,能耗降低了3.5653%.

超高速飞行器自适应模糊超螺旋控制律设计

针对超高速飞行器多源扰动下高精强鲁棒控制需求,提出了一种自适应模糊超螺旋控制律。首先,给出控制导向的超高速飞行器姿态模型;其次,采用广义超螺旋算法设计控制器,实现滑模变量与跟踪误差的高精收敛;同时,引入高阶齐次观测器估计飞行器复杂扰动,并在控制律中做前馈补偿以提高算法鲁棒性。针对控制增益选取问题,基于模糊算法,通过融合专家经验,建立控制增益与滑模变量直接关联。相较传统自适应方法,该自适应律可实现控制增益快速调节整定,同时兼顾系统响应速度、鲁棒性与抖振抑制需求。最后,以考虑复杂多源干扰的超高速飞行器为对象进行仿真,结果证明了自适应模糊超螺旋控制律的有效性。

基于IWOA的高速列车运行曲线节能优化研究

为降低高速列车运行能耗,使列车经过变坡点和变曲率点的附加阻力计算更精确、更贴近实际,提出了一种改进的鲸鱼优化算法(improved whale optimization algorithm,IWOA),对高速列车的运行曲线进行节能优化。首先,建立高速列车多质点附加阻力模型;其次,考虑满载率对列车运行能耗的影响,以准时、准点、限速为约束条件,构建新的能耗模型;然后,用IWOA对中间线路牵引-惰性工况转换点的最优位置进行搜索,IWOA引入混沌映射策略、自适应收敛因子和哈里斯鹰围攻机制,能有效提高算法的收敛速度和寻优精度,全局搜索能力显著增强,从而实现高速列车进一步节能运行的目的。最后,以合肥站—蚌埠站线路参数与CRH3型高速列车参数为实例,基于MATLAB平台进行仿真。仿真结果表明:采用PSO算法、GA算法和IWOA对高速列车运行曲线进行节能优化,优化后的曲线使列车运行能耗分别降低了7.81%,10.16%和15.95%。因此,IWOA对列车节能优化效果更加明显。

基于深度强化学习的高铁客票动态定价算法

为了解决需求函数未知情况下的高铁客票动态定价问题。以最大化单列车期望收益为目标构建Markov多阶段决策模型并设计DQN(Deep Q Net)强化学习框架寻找动态定价最优策略。算法以当日收益为奖励,通过神经网络来逼近所有状态-动作组合的期望最优收益。为验证算法性能,基于市场动态和旅客行为,开发高铁客运需求模拟系统并进行仿真实验。实验结果表明,智能体动态定价策略可以在不同需求水平下灵活调整价格,其性能接近理论上界并且显著优于对比策略。

发动机高速滚动轴承磨损故障信号特征识别

发动机高速滚动轴承在恶劣的工作环境下长时间使用,会导致其出现磨损故障,严重影响发动机的正常运行,且故障类型多样,每种故障类型都有其独特的特征,使得故障信号特征识别困难。为了有效解决这一问题,提出了一种发动机高速滚动轴承磨损故障信号特征识别方法。通过对发动机高速滚动轴承的振动信号进行EEMD分解和重建,获得其固有振动模态函数IMF,根据所得的IMF构建Hankel矩阵,获得拼接的奇异值样本特征矢量。通过划分样本空间和设置迭代阈值的方式,采用模糊聚类算法对故障样本聚类,计算出每个样本在不同聚类中的隶属度,以获得其贴近程度和故障特征。通过实验证明,所提算法能够较好的判别机械故障,准确性高,误报错报概率小,可确保设备的安全运行。

面向供需协同的城际通道高速列车开行方案优化研究

探讨城际通道短距离多线路列车优化问题,建立城际通道高速列车开行方案优化模型,提升列车运输效益和服务水平。针对城际通道线路特点,以多线路、多列车类型为基础构造列车流路径网络;分析旅客出行费用偏差,将客票费用偏差和旅行时间费用偏差相结合考虑,通过引入旅客对不同因素的容忍度参数,提出旅客出行需求与运输供给的协同偏差,进而建立供需协同的城际通道列车开行方案优化模型。以模拟退火算法为基础,设计模型求解算法。最后通过算例进行仿真验证,结果表明,利用该方法得到的开行方案,能在满足旅客需求条件下,在列车运输效率、运营收益和服务质量方面得到一定程度的提高,从而为高速铁路列车开行方案优化提供新思路。

基于数字孪生的高速列车辅助供电系统故障诊断方法研究

随着铁路技术的蓬勃发展,高速列车已经成为人们中长途出行的首要选择。同时,其伴随的安全性、舒适性问题也逐渐受到重视。辅助供电系统是高速列车能够正常运行的重要保障,系统故障将导致乘客舒适感下降,列车行驶受阻,铁路班次调整等一系列问题。为了避免此类情况的发生,本文主要对高速列车辅助供电系统故障诊断方法进行研究,基于数字孪生技术,建立辅助变流器仿真模型,模拟真实电路运行情况。然后以获取的故障数据集为分析对象,在MATLAB R2018a平台上训练多种机器学习模型。同时结合优化方法建立混合模型,比较诊断性能。实验结果表明,数字孪生技术能够大大降低收集数据的难度,可以快速获取数量多,类别丰富的故障数据;基于遗传算法的BP(back propagation)神经网络混合模型对类故障的诊断分类效果最好,平均精确率和准确率分别为86.7%和95.6%。基于数字孪生的机器学习模型诊断效率和精度高,可以运用在高速列车辅助供电系统的故障诊断过程中,保证列车安全行驶。