电动负载模拟器摩擦模型参数辨识方法

随机子空间算法作为模态参数识别算法中的主要方法之一,虽然被广泛运用于实际桥梁结构的模态参数识别中,但其依然存在一定的缺陷。基于此,针对其存在的三大问题：系统定阶难、只适用于时不变结构以及真实模态筛选存在主观性,笔者提出了相应的解决方法。首先,利用“奇异熵增量一阶导数法”实现系统阶次的智能化判定;其次,引入“滑窗技术”对输入信号进行划窗处理,实现时变结构的参数识别;然后,基于真实模态存在的一般规律,并通过建立相似矩阵实现真实模态的辨识;最后,将信号采集、信号预处理和改进随机子空间算法进行有效结合,运用于某大型斜拉桥振动台试验以验证所提算法的可靠性。结果表明：所提算法能运用于桥梁的健康监测中,且识别结果具有可信性。     

一种新的固有频率跟踪及虚假频率剔除方法

针对时变参数辨识中常见的固有频率辨识和虚假频率剔除问题,引入了一种基于新信息准则的子空间跟踪辨识算法,结合实验提出了一种消除虚假固有频率的后处理方法。首先,利用基于新信息准则的子空间跟踪算法辨识出伪时变模态参数;其次,通过聚类方法估计各阶伪固有频率;最后,利用滑动的数据窗比对数据剔除虚假频率。该方法仅需要给出预估的活动模态数即可。不同频率变化形式的仿真算例结果证明了本研究方法较其他辨识算法在信号信噪比较低时具有较高的辨识精度。高温环境下的时变模态实验也验证了该方法的可行性,说明该方法在实际工程中具有较高的应用价值。     

电动负载模拟器摩擦模型参数辨识方法

为实现电动负载模拟器的高精度控制,针对负载模拟器运行中受到的摩擦力影响问题,提出基于改进布谷鸟算法（improved cuckoo search algorithm,简称ICSA）的摩擦模型参数辨识方法。首先,搭建了电动负载模拟器试验样机,建立了动力学数学模型,并引入一种连续摩擦模型代替传统不连续摩擦模型;其次,将布谷鸟算法（cuckoo search algorithm,简称CSA）进行改进,在辨识中自动调整判定概率和步长的数值,提高了收敛速度和收敛精度;然后,通过逐点试验的方法得到了负载模拟器角速度范围为［-1,1］rad/s的摩擦力数据,并利用ICSA算法对摩擦力模型进行辨识;最后,进行了验证试验。试验结果表明： ICSA算法能准确快速地辨识出连续摩擦模型的6个参数,且收敛速度快、准确性高;当迭代达到最大迭代次数时,ICSA算法的目标函数值较CSA算法减小了45.2%。     

基于Hankel矩阵联合近似对角化的结构模态识别

针对基于二阶盲辨识(second order blind identification,简称SOBI)的模态参数识别方法存在的不足,提出了一种基于Hankel矩阵联合近似对角化(Hankel matrix joint approximate diagonalization,简称HJAD)技术的结构运行模态分析(operational modal analysis,简称OMA)的新方法。该方法通过对随机子空间类模态识别方法常用的Hankel矩阵进行联合近似对角化,以分离各阶模态响应,进行模态识别。与基于SOBI的模态识别方法相比,在具体实施过程中,仅需要在分析数据中添加与实测振动响应对应的时间延迟的数据,实现难度较小。数值算例和物理模型试验的分析结果表明,所提出的基于HJAD技术的结构运行模态分析方法,不仅具有鲁棒性强和计算效率高的优点,还可以克服传统的基于SOBI的模态识别方法的模态识别能力受测点数目限制的问题。     

REAL-TIME MODAL PARAMETER ESTIMATION USING SUBSPACE METHODS: APPLICATIONS

This article describes the underlying theory and hardware implementation of a newly developed algorithm for online modal parameter identification. An online modal parameter estimation algorithm using subspace methods is applied to both model and experimental data for a 4-m laboratory truss structure. Experimental evaluation of this algorithm demonstrates that the technique accomplishes the objective of tracking multiple modes of a complex dynamical system using multiple sensors. The time-varying behaviour is captured in real time via a graphical display of the frequencies and the damping ratios of the system. It is shown that the recursive algorithm provides results similar to the batch algorithm for a time-invariant system. In addition, it is shown that the batch algorithm used to derive the recursive algorithm performs similarly to a newly derived batch algorithm that is closely related to the Eigensystem Realization Algorithm. Details concerning the digital signal processor implementation and off-line monitoring are also presented.     

Recursive subspace identification for on-line tracking of structural modal parameter

The objective of this paper is to develop an on-line tracking of system parameter estimation and damage detection techniques using response measurements. To avoid the singular-value-decomposition in data Hankel matrix, a new subspace identification algorithm was developed. Seismic response data of a 3-story steel frame with abrupt change of inter-story stiffness from the shaking table test was used to verify the proposed recursive subspace identification (RSI) method by using both input and output measurements. With the implementation of forgetting factor in RSI method the ability of on-line damage detection of the abrupt change of structural stiffness can be enhanced. Then, the recursive stochastic subspace identification (RSSI) algorithm is also developed for continuous structural health monitor of structure by using the output-only measurements. Verification of the proposed RSSI method by using the white noise response data of a 2-story reinforced concrete frame from its low level white noise excitation was used. Discussion of the subspace identification model parameters is also investigated.     

环境激励下的旋转机械工作模态参数辨识

伴随着高速透平机械向大跨度、柔性结构方向发展,转子-轴承系统的稳定性将面临着严峻考验。在出厂测试阶段,确保机组转子系统具有足够稳定性裕度是降低生产现场机组发生失稳故障风险的重要手段。采用适用于随机平稳环境激励下的随机子空间法,对机组的模态参数进行辨识,可规避在转子非驱动端增设电磁激振器的传统测试方法。通过分析转子振型进动方向,区分一阶正反进动的模态参数。结合3-σ统计聚类算法,剔除非稳定的噪声或物理极点,形成了区分转子系统的正反进动的稳态图。数值仿真表明,随机子空间法可以有效地辨识系统的模态参数,利用旋转机械的振型进动方向分析方法可以区分正反进动。此外,通过传统扫频激励模态参数辨识试验,验证了随机子空间方法的辨识精度和工程测试可行性。研究结果可为透平机组的稳定性测试提供技术和理论支撑。     

The identification of a dam's modal parameters under random support excitation based on the Hankel matrix joint approximate diagonalization technique

Modal analysis is an effective method for monitoring a dam's health. Modal parameters can be identified from the measured vibration response of a dam under ambient excitation, such as that from an earthquake. In this paper, we first use the state space model to analyze the vibration of a dam under ambient support excitation and conclude that the nature excitation technique (NExT) can be used to the measured absolute acceleration response of the dam under band-limited stochastic support excitation to obtain its impulse response. To overcome some of the limitations of the traditional modal identification method for a structure under ambient excitation, we propose a modal parameter identification method based on the Hankel matrix joint approximate diagonalization (HJAD) technique. In this method, the Hankel matrix is defined as the covariance matrix of a vector, which is composed of measured acceleration responses, and their time-lagged data. This modal parameter identification method can be regarded as an improvement to the traditional time domain method because it introduces the joint approximate diagonalization (JAD) technique into the original method. On the other hand, the method can be regarded as an improvement to the SOBI-based modal identification method because it uses the Hankel matrix instead of the covariance matrix of response to perform the JAD. Therefore, this method combines the advantages of the two existing modal identification methods and overcomes some of their limitations. Compared with the SOBI-based modal parameter identification method, the implementation of the method presented in this paper is very convenient because we need only to add time-lagged response data to the analysis. The numerical analysis results show that the proposed modal parameter identification method based on HJAD technology not only has the advantage of a traditional blind modal parameter identification algorithm, but can also overcome the limitation of not being able to estimate more active modes than the number of available sensors. According to the satisfactory performance of this method in the analysis of strong-motion earthquake observation data for a gravity dam, the modal parameter identification method proposed in this paper has the potential for application in water conservancy engineering.     

某型瓦楞辊的现场振动测试与模态参数识别

针对瓦楞辊工作过程中产生的振动与噪声等现象,高速运行下在机架上对瓦楞辊传动系统进行了现场振动测试,使用随机子空间法从测试数据中识别了结构的前十阶模态频率及相应的振型和阻尼比,并与有限元分析结果进行比较。结果表明,通过机架的振动情况可较为准确地提取出上、下瓦楞辊的各阶频率;有限元模型能够准确地反映瓦楞辊的动态特性,可以为瓦楞辊结构的故障诊断及变形预测提供参考。     

USING CROSS—CORRELATION THEORY TO EXTRACT MODAL PARAMETERS IN FREQUENCY—DOMAIN

Conventional modal parameter identifications are usually based on frequency response functions, which require measurements of both the input force and the resulting response. However, in many cases, only response data are available while the actual excitations (such as wind/wave load) are not measurable. Modal parameters estimation must base itself on response-only data. Over the past years, many time-domain modal parameter identification techniques from output-only are proposed. A poly-reference frequency-domain modal identification scheme on response-only is presented. It is based on coupling the cross-correlation theory with conventional frequency-domain modal parameter extraction. An experiment using an airplane model is performed to verify the proposed method.     

移动最小二乘法的时变结构模态参数辨识

研究时变结构模态参数辨识,基于泛函矢量时变自回归模型(Functional series vector time-dependent AR model,FS-VTAR)提出一种改进的移动最小二乘法的时变结构模态参数辨识方法。该方法源于无网格法中构造形函数进行局部近似的思想,引入带权正交基函数对移动最小二乘(Moving least square,MLS)的基函数进行改进,使得在辨识时间域内构造形函数矩阵过程中不再出现数值条件问题,从而提高了计算精度。把时变系数在形函数上线性展开,利用最小二乘法得到形函数的系数,从而得到时变系数。把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明:改进的移动最小二乘法相比于传统的FS-VTAR模型能有效地避免基函数形式的选择和很高的基函数阶数且更加高效,相比于移动最小二乘法能有效地避免辨识过程中的数值问题,具有更高的模态参数辨识精度。     

REAL-TIME MODAL PARAMETER ESTIMATION USING SUBSPACE METHODS: THEORY

This article describes the underlying theory of a newly developed algorithm for online modal parameter identification. These online subspace estimation methods use eigenanalysis for data filtering, and are derived from a recent multi-input, multi-output batch algorithm. One method is obtained by deriving a new efficient data update expression combined with a recently developed modified singular value decomposition known as the URV method. The second method combines an existing data update expression with the URV method. The URV method enables recursive update of the signal subspace. The close relationship of a modified form of the batch estimation approach to the Eigensystem Realization Algorithm (ERA) is also shown through the introduction of an extended ERA method.     

基于改进自适应遗传算法的固定结合面动态特性参数优化识别

针对固定结合面传统建模方法精度较低和结合面的动态特性参数难以确定的问题,改进了基于弹簧阻尼单元的建模方法,提出了基于实验模态分析和改进自适应遗传算法的固定结合面动态特性参数的优化识别方法。该方法以有限元计算的理论固有频率和阻尼比与其对应实验模态分析结果的相对误差最小为目标函数,使用改进的自适应遗传算法优化识别固定结合面的刚度和阻尼参数。以自行设计制作的固定结合面模型为研究对象进行了建模、实验、参数识别等分析,分析结果表明：所提出的方法是正确的、有效的,参数识别误差在5%以内,达到了较高的识别精度。     

时间序列模型中的随机共振现象

在滑动自回归(auto regressive and moving average,简称ARMA)时间序列模型的基础上,利用模态稳定性图来确定系统真实模态,描述了在求解过程中产生的随机共振现象。借助悬臂梁的有限元模型,利用精细时程积分方法计算得到了其加速度脉冲响应函数,建立了用于振动模态识别的ARMA模型。在利用模态稳定性图来确定系统真实模态的过程中发现,加入合适的噪声信号可以有效地改善识别结果,剔除虚假模态,即产生了随机共振现象。对悬臂梁进行时变化处理后,随机共振现象较之前不变系统更加显著,对最终识别结果产生了明显的优化作用。     

液压伺服系统状态估计和参数辨识的鲁棒算法

针对液压伺服系统不易进行状态估计和参数辨识的问题,提出了一种鲁棒算法,把液压伺服系统的动态行为当作一个具有时变参数的线性随机状态空间模型来描述,把故障当作系统参数变化,将参数公式中重要项进行泰勒级数展开,推导线性状态方程和线性测量方程,从而得出状态向量和参数向量的估计。在液压伺服系统中实验结果表明:该鲁棒算法能很好地对液压伺服系统进行状态估计和参数辨识;并且相比于其他算法,收敛速度快,对非高斯噪声和系统参数故障的存在敏感性较低,鲁棒性好。     

环境激励下桥梁结构信号分解与模态参数识别

为实现环境激励下桥梁结构信号分解与模态参数识别的一体化,首先,针对现有集合经验模态分解算法存在的端点效应和有效本征模态函数筛选难的问题,通过引入镜像延拓算法和支持向量回归机算法来抑制端点效应,并根据互相关系数和能量系数建立筛选有效本征模态函数的新指标——有效系数;其次,根据桥梁结构真实模态存在的一般规律提出了用于智能化辨识稳定图中真实模态的算法;最后,通过某大型斜拉桥振动台试验来验证所提算法的可行性。结果表明,所提算法不仅能实现桥梁结构响应信号的自适应分解和重构,还能实现稳定图中真实模态的智能化筛选,即实现桥梁结构模态参数的智能化识别,且识别结果具有可靠性。     

大型压裂泵车车架实验应变模态分析方法研究

为解决某大型压裂泵车作业过程中存在的振动异常现象问题,将应变响应的随机状态空间模态识别技术应用到整车振动分析中。开展了实验应变模态分析,建立了振动应变响应与车架振动特性之间的联系,提出了消除整车振动剧烈的方法。以泵车压裂作业下三缸泵振动为激振源,进行了该大型压裂泵车车架振动试验,得到了车架在实际约束下的前六阶模态频率。研究结果表明:利用应变响应的随机子空间法可以较好地识别出约束状态下车架的固有频率;车架在实际约束状态下低阶固有频率在1.79 Hz~32.1 Hz之间,该频率与三缸泵激振频率存在重合区,是引起整车振动异常的主要原因。     

基于 RP-EKF 的无人机动力系统参数辨识

针对无人机动力系统电池电压波动导致系统噪声大、辨识结果精度低的问题,本研究提出了一种基于反向预测-增广卡尔曼滤波(RP-EKF)的无人机动力系统参数辨识方法。首先构建增广参数矩阵,将压降噪声模型考虑入辨识环节,其次提出反向预测卡尔曼滤波算法,设定新息平方比阈值,计算原始预测新息平方与反向预测新息平方的比值,通过对比预测新息比与阈值完成过程噪声调整并实现估计模型修正。实验结果表明,本文提出的基于RP-EKF的参数辨识方法,平均误差为39.22 rpm,均方根误差为55.85 rpm,平均相对偏差为0.85%,相比于最小二乘算法与卡尔曼滤波算法,本文方法辨识结果平均误差分别提高41.51%和22.26%,均方根误差提高49.63%和13.0%,平均相对偏差提高41.7%和22.7%。本文提出的算法拥有更高的辨识精度。     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

时序数据的随机子空间模型辨识和一步预测

时序数据是指按先后顺序排列的一组随机数据。在系统辨识中,一组时序数据对应于随机子空间模型的输出响应。本文依照工程实际,用随机子空间辨识技术来解决时序数据的参数化建模和预测问题,提出了用改进的Positive算法进行参数识别和一步预测,来替代原有的AR参数化建模和预测技术。最后通过一组丝杆误差时序数据,对算法作了仿真和验证。