Identification of Backflow Vortex Instability in Rocket Engine Inducers

Bayesian estimation is applied to the analysis of backflow vortex instabilities in typical three-and four bladed liquid propellant rocket(LPR)engine inducers.The flow in the impeller eye is modeled as a set of equally intense and evenly spaced 2D axial vortices,located at the same radial distance from the axis and rotating at a fraction of the impeller speed.The circle theorem and the Bernoulli’s equation are used to predict the flow pressure in terms of the vortex number,intensity,rotational speed,and radial position.The theoretical spectra so obtained are frequency broadened to mimic the dispersion of the experimental data and parametrically fitted to the measured pressure spectra by maximum likelihood estimation with equal and independent Gaussian errors.The method is applied to three inducers,tested in water at room temperature and different loads and cavitation conditions.It successfully characterizes backflow instabilities using the signals of a single pressure transducer flush-mounted on the casing of the impeller eye,effectively by-passing the aliasing and data acquisition/reduction complexities of traditional multiple-sensor cross correlation methods.The identification returns the estimates of the model parameters and their standard errors,providing the information necessary for assessing the accuracy and statistical significance of the results.The flowrate is found to be the major factor affecting the backflow vortex instability,which,on the other hand,is rather insensitive to the occurrence of cavitation.The results are consistent with the data reported in the literature,as well as with those generated by the auxiliary models specifically developed for initializing the maximum likelihood searches and supporting the identification procedure.

On-line detecting of transformer winding deformation based on parameter identification of leakage inductance

Transformers are required to demonstrate the ability to withstand short circuit currents.Over currents caused by short circuit can give rise to windings deformation.In this paper,a novel method is proposed to monitor the state of transformer windings,which is achieved through on-line detecting the leakage inductance of the windings.Specifically,the mathematical model is established for online identifying the leakage inductance of the windings by applying least square algorithm(LSA) to the equivalent circuit equations.The effect of measurement and model inaccuracy on the identification error is analyzed,and the corrected model is also given to decrease these adverse effect on the results.Finally,dynamic test is carried out to verify our method.The test results clearly show that our method is very accurate even under the fluctuation of load or power factor.Therefore,our method can be effectively used to on-line detect the windings deformation.

The Parametric Identification of a System with Unclear Input information

This paper develops an average power and energy method for the parametric identification of a system. The new method makes it possible to identify the parameters of a system depending only on its output information, and can be used in both linear and non-linear systems.

Online model identification of lithium-ion battery for electric vehicles

In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.

A METHOD OF ON-LINE MEASUREMENT FOR THE FREQUENCY RESPONSE AND IMPULSE RESPONSE FUNCTIONS OF MULTIVARIATE SYSTEM

In this paper,a method of multipoint pseudorandom combined excita-tion with the orthogonal reciprocal repeated sequences(ORRS)is presented on thebackground of the on-line identification of multivariate system.The capacity of therestraint to the identification error caused by the non-random D.C.drift of the mul-ti-input excitation with the ORRS in the multivariate system is also discussed.Thevalidity of the method described in this paper is proved by the modelling tests of themulti-plate rotor system.

Parameter Identification and Controller Design for the Velocity Loop in Motion Control Systems

Today the controller commissioning of industrial used servo drives is usually realized in the frequency domain with the open-loop frequency response. In contrast to that the cascaded system of position loop, velocity loop and current loop, which is standard in industrial motion controllers, is described in literature by using parametric models. Several tuning rules in the time domain are applicable on the basis of these parametric descriptions. In order to benefit from the variety of tuning rules an identification method in the time domain is required. The paper presents a method for the identification of plant parameters in the time domain. The approach is based on the auto relay feedback experiment by ?str?m/ H?gglund and a modified technique of gradual pole compensation. The paper presents the theoretical description as well as the implementtation as an automatic application in the motion control system SIMOTION. The identification results as well as the achievable performance on a test rig with a PI velocity controller will be presented.

Study of Synthesis Identification in Cutting Process with Fuzzy Neural Network

With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.

基于复杂度追踪的模态参数识别方法对比研究

复杂度追踪(complexity pursuit, CP)是求解振动信号盲源分离(blind source separation, BSS)问题的一类经典方法。用复杂度追踪估计解混矩阵主要有基于源信号复杂度计算的梯度下降(complexity pursuit-gradient descent, CP-GD)算法和基于时间可预测度的广义特征值分解(temporal predictability-generalized eigenvalue decomposition, TP-GED)算法。当前,这两种算法的关联性与算法性能尚缺乏研究,因此对这两种算法的等价性和计算性能进行了研究。首先,给出CP-GD和TP-GED两种算法的具体理论及算法流程;其次,利用二、三自由度振动系统直观地展示并对比解混向量对应的源信号复杂度及可预测度的变化规律;最后,通过对多工况下多自由度系统的模态参数识别算例,对比研究两种算法的精度及计算量。研究结果表明:在低阻尼比及高信噪比条件下,两种方法得到的解混矩阵是相同的;考虑到计算信号复杂度和梯度下降较为耗时,CP-GD算法计算代价要高于TP-GED算法。

制动闸片摩擦块孔结构对盘-块界面黏滑振动的影响

制动闸片摩擦块多采用孔结构以改善界面摩擦磨损行为及热分布特征。为进一步研究制动闸片摩擦块孔结构与盘-块界面黏滑振动特性的关系,通过开展摩擦学试验,分析了有孔和无孔摩擦块对界面黏滑振动的影响,并辨识了相应的Stribeck模型参数以描述盘-块界面间的摩擦因数特征。然后,建立了盘-块摩擦系统数值模型,基于辨识的Stribeck模型参数并结合理论分析,探讨了黏滑振动的关键影响因素,揭示了摩擦块孔结构对界面黏滑振动的作用机理。结果表明,摩擦因数-相对速度负斜率特征造成的负阻尼效应向系统输入能量,导致了系统的不稳定及黏滑振动。动、静摩擦因数的差值越大,负阻尼效应越强,系统黏滑振动强度及不稳定程度更高。相比无孔摩擦块,有孔摩擦块可通过孔结构调整界面摩擦特征,使动、静摩擦因数的差值减小,从而有效抑制界面黏滑振动强度,提高系统的稳定性。

基于改进型扩张状态观测器的永磁同步电机无模型预测电流控制

针对基于扩张状态观测器的永磁同步电机无模型预测电流控制策略在电机电感参数失配时电流控制性能下降、转矩脉动增大的问题,提出一种基于改进型扩张状态观测器的无模型预测电流控制策略。在扩张状态观测器中添加非参数模型电感辨识算法,该算法利用d轴扰动估计值计算得到电机辨识电感,将其反馈到下一时刻扩张状态观测器的电流与扰动估计中,消除电感失配对于传统扩张状态观测器的不利影响。将辨识电感用于无模型预测电流控制的输出电压指令的计算,进一步增强无模型预测电流控制的参数鲁棒性。搭建电机转矩控制系统进行实验,结果表明,该控制策略的参数鲁棒性强,具有良好的电流控制性能,在电机电感失配时能有效减小电机转矩脉动,具备较高的工程应用价值。

基于人体振动试验的坐姿人体模型研究

为了能够准确模拟人体受振特性,基于多体动力学原理,将人体简化成头部、上躯干、下躯干和腿臀部4集总质量、12自由度多刚体模型,模型中考虑上躯干、下躯干和腿臀部之间的腰部肌肉作用,各部分之间采用线性等效弹簧和阻尼进行连接。借助振动试验台,对8名志愿者进行了坐姿低频振动试验,得到了人体在垂向激励(0~20 Hz)和纵向激励(0~15 Hz)下座椅-头部的振动传递特性曲线。模型中模拟人体动态特性的48个刚度、阻尼参数,采用自适应模拟退火算法对试验得到的座椅-头部振动传递特性曲线进行参数辨识来确定。结果显示,12自由度多刚体动力学人体模型仿真曲线与试验曲线相一致,模型能够较为准确反映人体受振特性。

PARAMETRIC IDENTIFICATION AND SENSITIVITY ANALYSIS FOR AUTONOMOUS UNDERWATER VEHICLES IN DIVING PLANE

The inherent strongly nonlinear and coupling performance of the Autonomous Underwater Vehicles （AUV）, maneuvering motion in the diving plane determines its difficulty in parametric identification. The motion parameters in diving plane are obtained by executing the Zigzag-like motion based on a mathematical model of maneuvering motion. A separate identification method is put forward for parametric identification by investigating the motion equations. Support vector machine is proposed to estimate the hydrodynamic derivatives by analyzing the data of surge, heave and pitch motions. Compared with the standard coefficients, the identified parameters show the validation of the proposed identification method. Sensitivity analysis based on numerical simulation demonstrates that poor sensitive derivative gives bad estimation results. Finally the motion simulation is implemented based on the dominant sensitive derivatives to verify the reconstructed model.

Model predictive control with an on-line identification model of a supply chain unit

A model predictive controller was designed in this study for a single supply chain unit.A demand model was described using an autoregressive integrated moving average(ARIMA) model,one that is identified on-line to forecast the future demand.Feedback was used to modify the demand prediction,and profit was chosen as the control objective.To imitate reality,the purchase price was assumed to be a piecewise linear form,whereby the control objective became a nonlinear problem.In addition,a genetic algorithm was introduced to solve the problem.Constraints were put on the predictive inventory to control the inventory fluctuation,that is,the bullwhip effect was controllable.The model predictive control(MPC) method was compared with the order-up-to-level(OUL) method in simulations.The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.

Variable selection in identification of a high dimensional nonlinear non-parametric system

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

面向燃机多工况宽频域工作模态参数识别的随机子空间方法

面向燃气轮机工作状态下多工况、宽频域模态参数识别需求,在对典型燃机整机振动模态分析的基础上,虑及测试数据类型、测点位置与方向的选取及不同运行工况影响,提出一种针对燃机整机工作模态参数识别的随机子空间方法。基于实测振动数据,对典型燃机工作模态参数进行了自动划分识别。结果表明:通过分别控制行块数,充分挖掘位移、速度、加速度三种类型数据包含的不同频段模态参数信息并对识别结果择优合并,能够较好识别出数十倍于燃机工频的宽频域模态;合理选取测点位置和方向,能够得到关心频段内局部或整体模态;利用多转速运行工况数据,能够区分出受转速影响的整体模态。实现了对燃机整机振动宽频域范围内整体和局部模态、机匣和转子模态的识别,可为在此基础上的动力特性分析、整机动力学模型修正、结构振动状态评估、振动故障特征提取等提供支撑。

考虑坐标获取的试验模态分析的惯性参数识别方法

精准高效的坐标获取方式在模态分析的参数识别和性能优化等方面具有重要意义。基于模态试验中刚体振动的原理,分析其线参量和角参量的转换关系,推导出获取激励和响应坐标的方程,并结合质量线法得到惯性参数表达式。通过研究坐标获取误差的影响因素,搭建发动机动力学模型的仿真试验平台,进一步验证坐标获取与惯性参数识别的方法。以某型号变速箱总成为试验对象,利用基于最小二乘的卷积拟合算法(Savitzky-Golay, SG)对数据平滑滤波处理,对比电子三维坐标仪法、三维模型法与所提方法在坐标获取中的精度,然后选用质量线法识别惯性参数,并将其与MPC转动惯量测试平台计算的结果进行讨论。结果表明:相较传统方法,所提方法直接从模态试验中获取坐标,效率极大提升,成本显著降低,操作更加简便;同时几何坐标获取和质心坐标识别的最大误差在3 mm左右,转动惯量相对误差最大不超过7%,其精度优于部分测量方法,具有一定的工程应用价值。

Toward real-time digital pulse process algorithms for CsI(Tl)detector array at external target facility in HIRFL-CSR

A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.

一种具有学习功能的电器识别电路设计

针对传统用电器识别模式需要采集大量数据来建立模型,且只能识别电器参数差异较大用电器的弊端,通过对用电器的特征参量进行采集和分析,建立电器参数欧几里得空间及感知机模型,计算感知机模型的决策边界并构建出沃罗诺伊图,能够在学习模式下不断更新用电器的精确识别范围,并在识别模式下识别出不同种类的用电器。设计了一款简便快捷的用电器分析识别电路,识别电流区间为5 mA~10 A,并进行了实物识别试验,证明了设计的可靠性。

基于移动传感的桥梁自动模态参数识别

提出一种基于移动传感的桥梁自动模态参数识别方法。移动传感单元包括一个动力车和一个刚性小拖车,动力车搭载了数据采集和传输设备以及北斗授时模块,刚性拖车搭载加速度拾振器,动力车与拖车的连接具有良好的隔振效果,有效获取桥梁结构本身不同位置的振动信号。模态试验时,移动传感单元依次自动获取不同测点的结构振动信息,通过北斗授时模块实现与固定参考单元获取的振动信息相位时间同步。基于随机子空间的自动模态参数识别方法可以快速从固定参考单元与移动传感单元获取的振动信息中提取结构模态信息。该方法可以获取较高的空间分辨率结构振动信息,进而获取准确的结构振型信息。将移动传感应用于一座下承式系杆拱桥,取得了优良的模态识别效果。

基于机器学习的高速列车抗蛇行减振器劣化状态识别方法研究

为识别高速列车抗蛇行减振器服役过程中的劣化状态,首先基于运用统计选取了5种典型的组合参数,通过台架试验获取其动态频变刚度和阻尼特性;然后采用抗蛇行减振器非参数化建模方法建立了整车动力学联合仿真模型,计算得到不同工况下的车辆动力学响应;构建了机器学习分类问题,分别采用支持向量机(Support Vector Machine, SVM)和卷积神经网络(Convolutional Neural Network, CNN)的方法对减振器状态进行识别。研究结果表明,基于BP神经网络(Back Propagation Neural Network,BPNN)的非参数化模型更为准确地描述抗蛇行减振器的动态行为,建立的整车联合仿真模型计算结果与实测数据符合较好;采用SVM算法构建的机器学习模型识别效果一般,而采用CNN算法构建的机器学习模型则达到较高的识别准确度。考虑实际运用需求,将机器学习问题简化为6分类问题,信号通道数精简为4个,CNN机器学习模型仍可实现较高精度的劣化状态识别。