Modeling and parameter identification of amplitude- and frequency-dependent rubber isolator

A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to acquire these relations,force decomposition was carried out according to some sine vibration measurement data of nonlinear forces changing with the deformation of the rubber material.The nonlinear force is decomposed into a spring force and a damper force,which are represented by the amplitude-and frequency-dependent spring and damper coefficients,respectively.Repeating this step for different measurements gives different coefficients corresponding to different amplitudes and frequencies.Then,the application of a parameter identification method provides the requested approximation functions over amplitude and frequency.Using those formulae,as an example,the dynamic characteristic of a hollow shaft system supported by rubber rings was analyzed and the acceleration response curve in the centroid position was calculated.Comparisons with the sine vibration experiments of the real system show a maximal inaccuracy of 8.5%.Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.

Parameter Identification of Rubber Isolators Using Frequency-Dependent Spring and Damper Coefficients

A model to describe the hysteresis damper character of rubber material is presented in this paper. It consists of a parallel spring and damper, whose coefficients change with vibration frequencies. In order to acquire these relations, the force decomposition is carried out according to some sine vibration measurement data about nonlinear forces changing with deformations of the rubber material. The nonlinear force is decomposed into a spring force and a damper force, which are represented by a frcquency-dependent spring and damper coefficient, respectively. Repeating this step for different measurements will give different coefficients corresponding to different frequencies. Then, application of a parameter identification method will provide the requested functions over frequency. Using those formulae, as an example, the dynamic character of a hollow shaft system supported by rubber rings is analyzed and the acceleration response curve in the centroid position is calculated. Comparisons with sine vibration experiments of the real system show a maximal inaccuracy of 8. 8 %. Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.

Parameter identification of hysteretic model of rubber-bearing based on sequential nonlinear least-square estimation

In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB （GZN 110） under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least- square estimation （SNLSE）, is introduced to identify the model parameters. It is shown that the proposed simplified Bouc- Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.

Off-Line Identification of Induction Motor Parameters

This study presented an off-line identification method of induction motor (IM) parameters. Before startup,the inverter drive performed automatically a modified DC test, a locked-rotor test, a no-load test and a step-voltage test to identify all the parameters of an induction motor. No manual operation and speed signals were required in the process. In order to obtain effective messages and improve the accuracy of identification, the discrete fast Fourier transform (DFFT) and the least-squares were used to process the signals of currents and voltages. A phase-voltage measuring method for motors was also proposed, which measured directly the actual conducting time of three upper switches in the inverter without need for a dead-time compensator. The validity, reliability and accuracy of the presented methods have been verified by the experiments on a VSI-fed IM drive system.

Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

A Technology for Online Parameter Identification of Permanent Magnet Synchronous Motor

Accuracy of the motor parameters is important in realizing high performance control of permanent magnet synchronous motor(PMSM).However,the inductance and resistance of motor winding vary with the change of temperature,rotor position and current frequency.In this paper,a technology based on circuit model is introduced for realizing online identification of the parameter of PMSM.In the proposed method,a set of nonlinear equations containing the parameters to be identified is established.Considering that it is very difficult to obtain the analytical solution of a nonlinear system of equations,Newton iterative method is used for solving the equations.Both the simulation and testing results confirm the effectiveness of the method presented.

基于OFDM信号的MIMO雷达反侦察性能分析

电子侦察系统在侦收到雷达信号以后通过测量信号的到达时间、脉冲幅度、脉冲宽度、载频和到达角等5个参量形成脉冲描述字信息,实现对雷达信号的分选与识别。对采用OFDM信号的MIMO雷达抗分选与识别性能开展研究,通过建立电子侦察系统接收到的MIMO雷达信号模型,分析电子侦察系统对MIMO雷达信号的参数测量性能。理论研究与仿真结果表明,与传统的雷达信号相比,采用OFDM信号的MIMO雷达信号幅度起伏比较大,降低了电子侦察系统对信号参数的测量性能,具有更好的抗分选识别性能。

Daytime precipitation identification scheme based on multiple cloud parameters retrieved from visible and infrared measurements

Visible and infrared(VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner(VIRS) and precipitation radar(PR) aboard the Tropical Rainfall Measuring Mission(TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness(COT), effective radius(Re), and brightness temperature of VIRS channel 4(BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information(PISCP). It is essentially a lookup table(LUT) approach that is deduced from the optimal equitable threat score(ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds(PCs) and non-precipitating clouds(N-PCs) are well distinguished by PISCP, with the probability of detection(POD) near 0.80, the probability of false detection(POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.

基于飞行实测载荷的副翼操纵导数识别

某型飞机横向操纵时副翼和扰流板会耦合偏转实现飞机的滚转运动,现有气动力参数辨识方法无法单独识别出副翼和扰流板的操纵导数。基于飞机控制律设计逻辑和滚转运动时结构的受载特点,提出一种基于飞行实测载荷的副翼操纵导数识别方法。通过实测机动飞行中机翼各测载剖面的结构载荷并结合剖面外结构质量与加速度分布,提出了机动飞行中机翼气动载荷的测量方法;结合飞机横向操纵瞬时滚转力矩平衡方程和操纵面偏转逻辑,推导了滚转改出时副翼操纵导数识别过程,提出了基于机翼实测载荷的副翼操纵导数识别方法;基于前述方法开展了副翼操纵导数识别,并分析了不同马赫数对副翼操纵导数的影响。研究表明,基于飞行实测载荷能够进行副翼操纵导数识别,而且同一马赫数下副翼操纵导数识别结果集中度较好,随着马赫数的增加,副翼操纵效能将会降低。飞行载荷实测结果可用于气动参数辨识工作中。

基于老化补偿的功率模块全生命周期在线结温监测方法

功率模块结温在线监测对提升系统性能和可靠性具有重要意义。应用导通饱和压降和电流对结温进行估计,对测量带宽要求不高,对硬件和算法的侵入性低,是比较理想的结温在线监测方法。但是传统的测量方法很难满足结温精度要求,另外长期工作下功率模块老化会引起温度模型参数漂移,进一步加剧结温估计误差。为此,该文提出一套高精度测量电路和采样校准策略,应用温漂抑制、动态自适应采样、误差定向标定、同步时序控制等策略,大幅提高了在线采样分辨率和结温估计精度。针对老化问题,提出一种基于大电流注入的急停控制策略,解决了老化特征参数在线辨识问题,实现了结温模型的老化补偿,大大提高了老化后的结温监测精度,实现了全生命周期高精度在线监测。最后,在250kW三相变流器平台上进行了实验验证,得到老化前后的结温估计误差,证明了该文方法对提高结温监测精度具有明显效果。

台风作用下深圳新世界中心风致响应实测与风洞试验研究

依据近10年来监测到的5次强台风过境期间深圳新世界中心的实测响应数据,分析了该建筑结构在台风影响下的响应特点和模态参数变化特性,并将实测结果与粗糙度指数α分别为0.22,0.30和0.35地形下的风洞试验结果进行了比较。结果表明:历次台风影响下实测建筑达到最大振幅附近时均表现为明显的横风向振动,且最大振动方向均为南北方向;实测最大峰值加速度为17.28 cm/s^(2),满足舒适度要求;台风过境时,建筑结构模态频率表现出明显的振幅依赖性和时变特性,采用“时变”方式描述更加合理,几次台风过程的结构模态频率均是随时间先减小,在最大风速时达到最小值,然后增大并恢复到常态值;结构模态阻尼比在中低振幅区域分布较为离散,随着振幅的增加,阻尼比有所增大,顺风向和横风向的阻尼比最大分别为1.9%和1.2%;实测结果更接近于α为0.35地貌的风洞试验结果,显示C类地貌的风洞试验结果偏于保守。

锚固钻孔软弱夹层结构特征智能识别方法

【目的】锚固孔钻进作成孔质量深受岩体内存在的软弱夹层结构面的影响,这些软弱夹层往往是岩体中的隐患,影响钻进的稳定性和效率,关系到整个支护结构的稳定性和安全性。当钻头钻进软弱夹层时,其动态响应特征会变得明显起来,这种响应反映了钻进过程中岩体结构的变化情况。【方法】基于钻进比能的研究视角,利用自主研发的随钻测量系统,开展含有软弱夹层的类岩体试件数字钻探试验。这一系统能够实时、准确地监测并记录随钻过程中的各项参数,如钻压、扭矩、转速以及钻进速度等。通过对这些参数的敏感度分析、主成分分析和熵值分析,评估不同参数对软弱夹层响应特征的反馈程度,从而确定了影响最为显著的参数指标。其次,基于显著参数指标,建立钻进功率比识别法,实现类岩体软弱夹层智能化识别。【结果和结论】研究结果表明:(1)扭矩参数在随钻破岩和软弱夹层特性识别中起主要作用,其权重最大,反馈度效果最佳。(2)以扭矩参数变化为主要识别特征而建立的钻进功率比识别法,成功将软弱夹层厚度识别的平均准确率由原先的72.943%提高至88.268%,同时基于钻进功率比提出的倾角识别法,经计算倾角识别平均准确率高90.833%,软弱夹层特性识别精确度得到显著提高。这一显著的提升不仅证明了新方法的有效性,也为实际工程中的岩体软弱夹层识别与处理提供了准确和可靠的依据。

基于图优化参数辨识的船体变形测量方法

针对船体变形惯性匹配测量中模型参数准确性影响测量精度的问题,本文提出了一种基于图优化的船体变形模型参数辨识方法并应用于惯性匹配测量。通过分析船体变形模型预设参数对惯性匹配测量的Kalman滤波影响,得到参数对惯性匹配精度的影响机制;利用船体变形历史数据,结合待辨识的船体变形模型参数组成图优化超图,建立船体变形参数辨识的图优化模型,实现船体变形惯性匹配预设模型参数的辨识,最后将参数辨识结果代入惯性匹配方程以完成准确测量。仿真实验验证了该方法可以有效地完成船体变形参数辨识,保障船体变形惯性匹配测量的准确度。

基于参考键相动态追踪的无转速叶尖定时测量方法

针对叶尖定时系统在测量转子叶片振动过程中转速同步传感器安装困难的问题,提出基于参考键相动态追踪的叶尖定时测量方法。假设整级转子叶片间存在随机失谐,则在变转速工况下各叶片并非同时达到共振状态,在每转内,利用各叶片到达传感器的实测到达时间,计算叶片在相邻叶尖定时传感器间的局部平均转速与转子整圈平均转速的偏差值,判断同级所有叶片的振动状态,选择几乎不振动的叶片作为该圈的参考键相,计算目标叶片在该圈的振动位移值,依此类推,计算各圈目标叶片的振动位移,实现转子叶片在无转速参考下的非接触式振动测量。试验验证结果表明,与传统叶尖定时方法相比,利用该方法计算出的叶片振动位移所辨识出的叶片振动频率相对误差小于0.2%,振动位移相对误差小于10%,说明基于参考键相动态追踪的叶尖定时测量方法不仅能在无转速参考的情况下准确计算出叶片的振动位移,更能准确地辨识出叶片振动参数。

基于双目视觉的卫星太阳翼模态辨识方法

在解决卫星柔性太阳翼模态辨识问题时,传统接触式测量方法会对太阳翼本身动力学模型造成影响,附加测量靶标的方法,增加了额外的物理元器件和系统的复杂性,且不便于在轨实际应用。鉴于此,本文提出一种无接触、无标识点的光学测量方法。首先,用星载双目视觉系统采集柔性太阳翼振动图像,并提取太阳翼边缘角点作为特征点,测量这些特征点在时域下的位置变化情况和速度变化情况。然后,利用特征系统实现算法(ERA)来辨识太阳翼模态信息。最后,设计了物理模型试验。试验结果表明:本文提出的双目视觉测量方法装置简单,测量精度高,能够完成对太阳翼的模态辨识,模态辨识误差约为3.797%。本文所提出的基于双目相机的太阳翼模态辨识方法,为空间大挠性结构振动参数的在轨测量提供了参考。

地震动演变功率谱模型参数的统计建模

工程上常将规范反应谱拟合的确定性取值作为地震动演变功率谱参数,这虽然便于工程应用,但同时存在明显弊端:一方面,参数并非从实测强震记录中获得,具有较强的经验性;另一方面,确定性参数生成的地震动样本过于规则,且工程特性单一,无法全面地反映地震动的随机性。为了克服上述难题,从PEER的NGAGWest2地震动数据库中筛选1766条主轴方向的实测强震记录,并根据《中国地震动参数区划图》建议的场地类别和聚类方法划分为15组;随后,识别实测强震记录的演变功率谱参数,并结合K-S检验和BIC信息准则,确定每个参数最优概率模型;最后,根据演变功率谱参数统计建模结果,结合降维方法,生成了Ⅱ类场地与Ⅲ类场地的代表性时程。与《建筑抗震设计规范》中建议取值不同,研究参数来源于实测地震动,且具有随机性,避免地震动样本工程特性的单一化和规则化。

一种无线电信号的模拟调制参数测量系统设计

无线电信号的模拟调制参数测量在无线电检测定位、通信发射机测试校准,民用船舶的导航通信等领域都有广泛应用。设计实现了一种基于超外差结构的无线电信号模拟调制参数的测量系统,以STM32F4系列单片机作为控制核心,包括的主要硬件电路模块有本振信号源、混频器、LC无源滤波器、锁相环、低噪声放大器、包络检波电路、鉴频电路等。系统可自动识别普通调幅波、调频波、未调制的单频载波等模拟调制类型,完成无线电信号解调输出的同时自动测量出载波频率、调制信号频率、调幅指数、调频指数等主要调制参数。实测结果表明,系统对调制类型识别正确,调幅或调频信号的解调波形信噪比高、无明显失真,载波频率测量的相对误差小于10-5,主要调制参数测量的相对误差均小于5%。

基于随机相位差谱的全非平稳地震动建模

为合理表征地震动过程的全非平稳性和随机性,发展了一类基于相位差谱和功率谱结合的随机地震动模型。首先,阐明了相位差与地震动全非平稳性的关系,并利用实测强震记录,进行相位差谱模型参数的识别及统计分析。其次,采用确定性和随机性参数的功率谱模型,分别获得确定性参数的取值以及随机性参数的归一化最优概率分布。最后,通过选取基本随机参数的代表性点集,获得相应的地震动加速度代表性时程集合。研究结果表明:仅需2个或4个基本随机变量即可模拟具有自然变异性及丰富概率信息的地震动加速度过程,且模拟所得的加速度反应谱与实测强震记录拟合良好,为应用概率密度演化理论进行复杂工程结构的随机地震反应及抗震可靠性精细化分析奠定基础。

基于基波同步相量的次/超同步振荡参数辨识探究:频谱特性和关键问题

大量新能源发电和电力电子设备引起的次同步振荡(SSO)严重影响了电力系统稳定性。基于广域测量系统(WAMS)和相量测量单元(PMU)提供的基波同步相量辨识次/超同步振荡参数具有广域同步和高刷新率的核心优势,可实现次/超同步振荡的动态在线监测。该文从基波同步相量在次/超同步振荡下的复数域频谱特性出发,综述并分析了目前已有次/超同步振荡参数辨识方法的参数辨识特性及关键问题。首先,根据同步相量频谱分析了频率辨识范围、各分量叠加耦合特性及相位翻转特性;其次,分析了参数辨识的关键问题在于振荡分量耦合关系、基波分量耦合关系的解耦问题及频谱泄漏问题,并围绕复数域频谱分析方法分析了在解决以上问题时的三个关键难点:针对次/超同步分量的耦合关系有效地辨识超同步分量的问题、不同同步相量采样频率下进行参数辨识的适应性问题,以及如何处理频谱泄漏从而缩短数据窗长的难题;对此,综述了相应解决方法并展望了未来可能的技术突破点;最后,通过仿真数据和实际PMU数据对比分析验证了该文观点。

考虑电流采样误差的数控机床PMSM参数辨识策略

作为数控机床的重要执行机构,伺服电机本体参数信息的准确获取对工业母机加工性能有着决定性作用。现有参数辨识策略多仅考虑逆变器非线性这一非理想因素对辨识精度的影响,忽略了离散控制系统电流采样误差对辨识结果的精度影响。针对这一问题,对电流采样误差(偏置误差及比例误差)对本体参数辨识精度影响进行分析;结合数控机床的实际运行工况,在无需进行电流采样误差修正的前提下,设计参数辨识步骤,利用有效辨识过程中的代数平均及误差消减方法,有效规避电流采样误差对辨识精度的影响。实验结果表明:在无需进行电流采样误差补偿及硬件修改的前提下,所提方法可实现电机本体参数准确离线辨识,且辨识误差均低于5%,可为数控机床伺服系统系统初始化过程提供有效保障。