Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related techniques.According to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and compared.The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated.The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given.Key issues on OFRMPC optimizations for LPV systems are discussed.Furthermore,the future research directions on OFRMPC for LPV systems are suggested.

Identifi cation of model structure parameters via combination of AFMM and ARX from seismic response data

To identify the model structure parameters in shaking table tests from seismic response, especially from time- varying response records, this paper presents a new methodology by combining the online recursive Adaptive Forgetting through Multiple Models （AFMM） and offtine Auto-Regression with eXogenous variables （ARX） model. First, the AFMM is employed to detect whether the response of model structure is time-invariant or time-varying when subjected to strong motions. Second, if the response is time-invariant, the modal parameters are identified from the entire response record, such as the acceleration time-history using the ARX model. If the response is time-varying, the acceleration record is divided into three segments according to the accurate time-varying points detected by AFMM, and parameters are identified by only using the tail segment data, which is time-invariant and suited for analysis by the ARX model. Finally, the changes in dynamic properties due to various strong motions are obtained using the presented methodology. The feasibility and advantages of the method are demonstrated by identifying the modal parameters of a 12-story reinforced concrete （RC） frame structure in a shaking table test.

Iterative regularization method for image denoising with adaptive scale parameter

In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

变化环境下渭河流域土壤水储量模拟及影响因素

[目的]随着气候变化和人类活动影响加剧,流域气候和下垫面条件发生改变,为准确模拟土壤水分变化过程并分析其影响因素对于地区的水资源管理和植被建设的重要意义。[方法]以黄土高原地区渭河流域为研究对象,基于ABCD水文模型,采用EFAST法分析模型参数敏感性,构建常参数和时变参数模型,比较不同参数模型的径流深和土壤水储量模拟精度,探讨气候变化和植被恢复对渭河流域土壤水储量变化的影响,明确土壤水储量变化的主导因素。[结果]土壤水层补给地下水的比例参数c最为敏感,地下水储放系数d、流域实际蒸散发量与土壤蓄水量之和的上限参数b次之,表征土壤完全饱和前径流的倾向性参数a最不敏感;与ABCD常参数模型相比,时变参数模型将率定期径流深模拟结果的NSE、KGE、R^(2)分别提高19%,10%,19%,验证期的NSE、KGE、R^(2)分别提高7%,7%,9%,时变参数模型显著改善径流深模拟效果;基于时变参数模型模拟的渭河流域土壤水储量与ERA5-Land 0—100 cm土层土壤水储量的相关关系最强,二者的变化过程基本相同;不同影响因素对渭河流域土壤水储量变化的影响程度由强到弱依次为潜在蒸散发>降水>NDVI。[结论]研究成果为该地区水资源规划和管理、植被建设提供科学依据,也为其他类似地区的土壤水储量研究提供参考。

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

“双支柱”政策调控与企业金融资产配置时变关系研究:基于TVP-SV-VAR模型的实证检验

金融科技发展和市场制度变革给传统金融体系带来的巨大冲击,使得“双支柱”政策调控的有效性面临严峻考验。借助TVP-SV-VAR模型,实证检验“双支柱”政策调控对企业金融资产配置的动态脉冲效应。研究表明,货币政策和宏观审慎政策的调控效果呈现出阶段性的演化特征,二者的协同效应能够有效抑制企业金融资产的过度配置。相比制度环境冲击,“双支柱”政策调控在面对金融环境冲击时表现出更强的作用效果和力度。进一步研究发现,“双支柱”政策通过资本流动管理、资产价格稳定和金融风险缓释途径影响企业金融资产配置,且对流动性与非流动性金融资产配置的作用效果存在显著差异。研究结论为理解实体企业金融化和完善金融市场制度提供了新的理论视角,也为政府相关部门依据金融环境和制度环境进行“双支柱”政策调控的动态调整提供决策依据。

锂离子电池的等效时变内阻模型及应用

针对锂(离子)电池在恒流、恒压和/或恒功率等工况下,Thevenin等效电路模型无法在线辨识其模型参数,进而无法描述锂电池特性的问题,提出了一种新的等效时变内阻模型。分析表明,通过将锂电池的开路电压和内阻,描述成未知的时变电压和内阻,并利用安时积分和随机漫步模型,分别表示其未知时变电压和内阻的进化,就可为锂电池建立一种以状态空间进行描述的等效时变内阻模型。分析也表明,因电池充放电而产生的暂态极化电压,可由电池端电流与时变内阻的乘积,以及电池端电压观测噪声分布的组合进行描述。本文模型相比传统模型,在恒流恒压以及恒功率的工况下,SoC估计的均方根误差平均降低了48%。公开充放电测试数据集和实验的SoC估计,均验证了本文模型及分析结果的正确性和有效性。

水泥乳化沥青混合料路面压实工艺参数研究

为了精准预测水泥乳化沥青混合料路面达到目标密实度所需的压路机压实工艺参数组合,基于该混合料压实流变时变特性及压实Bodner-Partom(B-P)本构模型,采用数值仿真和路面压实试验相结合的方法开展研究。首先对ANSYS进行二次开发得到B-P材料模型,然后将阶段性变化的B-P模型参数代入压实组合仿真分析中,最后开展路面压实试验,进一步得到压实工艺参数优化组合。数值仿真研究结果表明:由仿真结果得到3个低频高幅高速组合,3个组合作用后沿轮宽方向的混合料竖向应变值减小幅度均较大,在路面深度6 cm处的混合料竖向应变衰减幅度值均较小,其中组合六的衰减幅度值为最小,路面目标成型厚度可控制在7 cm以下。路面压实试验表明,组合六(低频高幅高速压实6遍)的有效压实能量偏大,应将组合六的压实遍数优化为:低频高幅高速压实5遍+高频低幅高速压实2遍。

基于LPV-MPC的风电机组功率-载荷协调控制策略

大型风电机组变桨控制是一个复杂的非线性控制任务,如何在系统约束和风速扰动的条件下实现功率调节和载荷降低成为难题。针对该问题,文章采用机理与参数辨识方法构建了一个面向控制的线性变参数(LPV)模型,LPV模型引入间隙度量理论来降低模型复杂度,为控制设计提供了更加精确的线性模型。设计了时变卡尔曼滤波器,实现了风机的最优状态估计,基于获得的最优状态,提出了自适应模型预测(MPC)变桨控制器,MPC控制器能够根据实时风速变化调整预测模型,在满足系统约束的条件下实现大型风电机组功率-载荷的协调控制。OpenFAST对比仿真实例表明了模型与控制器的有效性。

人民币对“一带一路”沿线国家锚效应的实证研究

文章基于修正的二阶段Frankel-Wei模型,在其第二阶段回归分析中构建时变参数状态空间模型,对人民币及其他主要世界货币对“一带一路”沿线国家的货币锚效应进行样本期的分段动态检验、对比分析。根据检验结果,从中国自身和中国同沿线国家关系两个层面遴选指标变量,并构建二阶段Heckman模型研究人民币对“一带一路”沿线国家整体及“一带”沿线和“一路”沿线两个区域货币锚效应的影响因素。研究结果显示:人民币对沿线国家的货币锚效应已超过英镑,和日元基本相当,但和美元、欧元之间尚有较大的差距;目前,人民币对“一路”沿线的锚效应整体高于“一带”沿线国家;虽然人民币对沿线国家的锚效应水平不高且并不稳定,但在“一带一路”倡议提出并实施以来,其产生显著锚效应的沿线国家数量有较多的增加;且在沿线国家中,产生显著锚效应的区域也已逐渐形成。人民币对“一带一路”沿线货币锚效应影响因素的检验结果显示:无论是“一带一路”沿线整体还是“一带”沿线及“一路”沿线两个区域,在是否选择锚定人民币及已锚定人民币的国家中,人民币锚效应大小的影响因素均存在一定的差异。据此,文章进一步探讨未来该如何进一步提升人民币对沿线国家的货币锚效应。

基于时变参数的灰色模型在变形监测中的应用

针对传统灰色GM(1,1)预测模型拟合和预测精度较低的问题,建立了基于时变参数的灰色模型。首先介绍了传统灰色GM(1,1)和二阶GM(2,1)模型的建立过程;然后基于时变参数优化灰作用量的原理,建立了基于时变参数的灰色GM(1,1)模型和GM(2,1)模型;最后以两组矿区地表沉降监测数据为例,利用不同灰色模型方案进行预测对比,并评定了灰色模型的精度。结果表明,基于时变参数的GM(2,1)模型预测精度最高,灰色模型精度检验等级为Ⅱ级,说明该模型更加贴合矿区地表监测数据变形趋势。

基于两点模型的堆芯功率多目标保性能控制

现阶段能源产业需要核电站能够根据实际功率需求对输出功率进行调整,因此压水堆堆芯负荷跟踪运行是现阶段压水堆商业化运行的重要需求之一.为了避免运行过程中由于堆芯功率分布不均匀导致的氙震荡、热点等问题,对堆芯运行过程中产生的轴向功率偏差进行控制也是压水堆安全运行的关键.基于中子动力学方程、反应性反馈方程和热工水力学方程,结合堆芯功率空间耦合构建了压水堆堆芯功率两节点模型,并在这一模型的基础上增加负荷跟踪和轴向功率偏差跟踪量.根据这一模型设计了能够实现负荷跟踪控制和轴向功率分布控制并且具有抗时变性的LPV-LQR多目标控制器.在经典的多个瞬态工况下进行仿真的结果表明,LPV-LQR控制器具有良好的负荷跟踪控制和轴向功率分布控制能力,并且能够更好适应堆芯功率系统的时变性.

考虑转速及温度影响的磁轴承分散PID控制器参数优化设计

传统磁轴承控制器设计过程多使用定参数磁轴承-转子系统动力学模型,难以保证良好的控制器性能。为此,考虑磁悬浮旋转机械运行过程中转速及温度对系统模型参数的影响,建立了变参数系统模型,进而对磁轴承分散PID控制器参数进行了优化设计。首先,转速影响轴承气隙的原理是材料的泊松效应,温度则是通过定转子热膨胀位移差影响轴承气隙,由此建立轴承支承力变参数模型;其次,考虑温度对转子材料弹性模量的影响,建立了考虑转速及温度影响的变参数系统模型;再次,以控制系统广义幅值裕度及灵敏度的加权值为优化目标,使用遗传优化算法对分散PID控制器参数进行了优化设计;最后,在测试平台上验证了所提磁轴承控制器参数优化设计方法的有效性。

面向变形飞行器的时变气动参数在线辨识方法

针对变形飞行器快时变气动参数的在线高精度获取问题,本文提出一种基于BP神经网络模型的气动参数在线辨识方法。基于变形飞行器气动模型的非线性输入/输出映射关系,建立能够在一定精度范围内逼近变形飞行器气动模型的BP神经网络模型。根据在线实测动力学参数观测数据,采用扩展卡尔曼滤波方法在线训练神经网络,实时校正并获取神经网络模型参数,基于神经网络模型快速计算并预测气动参数,从而跟踪快时变、非线性气动模型的变化。通过对变形飞行器连续变形/构型突变的气动参数辨识进行数学仿真验证。结果表明:提出的方法收敛速度快、在线辨识精度较高,可以实现对变形飞行器气动参数的有效辨识。

时变参数灰色模型在变形监测中的应用

为提高沉降数据的准确预测并分析监测目标的变形趋势,避免重大工程事故的发生,建立了基于优化背景值的时变参数的灰色模型。首先,介绍了传统灰色GM(1,1)模型与新陈代谢灰色模型;其次,基于时变参数优化灰作用量的原理,建立了基于时变参数的灰色GM(1,1)模型和新陈代谢灰色模型,并采用黄金分割法优化灰色模型中权重参数,建立优化背景值的时变参数的灰色模型;最后,以某矿区地表沉降监测数据为例进行预测结果验证与精度评定。实验结果表明,基于优化背景值的时变参数的灰色模型预测精度最高,更加贴合地表监测数据变形趋势。

采用联合算法识别人-桥耦合系统时变模态参数

为更加准确地识别在役人行桥结构的时变模态参数,提出了一种基于联合方法的人行桥瞬时频率和时变阻尼比识别方法。首先,采用联合方法分别识别模态分量信号的瞬时频率和自由衰减振动信号的时频系数。然后,通过推导瞬时频率、时频系数模值和阻尼比三者之间的函数关系,估算获得人-桥耦合系统的时变阻尼比。最后,通过人-桥耦合数值算例和华威桥步行试验对联合方法进行了验证。结果表明:联合方法可以有效改善多种复杂因素下自由衰减振动信号提取结果不准确的问题,并且该方法识别的瞬时频率和时变阻尼比曲线可以清晰地表征行人空间位置变化对钢-混组合人行桥频率和阻尼比的变化趋势。

LVP Modeling and Dynamic Characteristics Prediction of A Hydraulic Power Unit in Deep-Sea

A hydraulic power unit （HPU） is the driving ＂heart＂ of deep-sea working equipment. It is critical to predict its dynamic performances in deep-water before being immerged in the seawater, while the experimental tests by simulating deep-sea environment have many disadvantages, such as expensive cost, long test cycles, and difficult to achieve low-temperature simulation, which is only used as a supplementary means for confirmatory experiment. This paper proposes a novel theoretical approach based on the linear varying parameters （LVP） modeling to foresee the dynamic performances of the driving unit. Firstly, based on the varying environment features, dynamic expressions of the compressibility and viscosity of hydranlic oil are derived to reveal the fluid performances changing. Secondly, models of hydraulic system and electrical system are accomplished respectively through studying the control process and energy transfer, and then LVP models of the pressure and flow rate control is obtained through the electro-hydraulic models integration. Thirdly, dynamic characteristics of HPU are obtained by the model simulating within bounded closed sets of varying parameters. Finally, the developed HPU is tested in a deep-sea imitating hull, and the experimental results are well consistent with the theoretical analysis outcomes, which clearly declare that the LVP modeling is a rational way to foresee dynamic performances of HPU. The research approach and model analysis results can be applied to the predictions of working properties and product designs for other deep-sea hydraulic pump.

HOSVD-based LPV modeling and mixed robust H_2/H_∞ control design for air-breathing hypersonic vehicle

This paper focuses on synthesizing a mixed robust H_2/H_∞ linear parameter varying（LPV） controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition（HOSVD） approach.The design of hypersonic flight control systems is highly challenging due to the enormous complexity of the vehicle dynamics and the presence of significant uncertainties.Motivated by recent results on both LPV control and tensor-product（TP） model transformation approach,the velocity and altitude tracking control problems for the air-breathing hypersonic vehicle is reduced to that of a state feedback stabilizing controller design for a polytopic LPV system with guaranteed performances.The controller implementation is converted into a convex optimization problem with parameterdependent linear matrix inequalities（LMIs） constraints,which is intuitively tractable using LMI control toolbox.Finally,numerical simulation results demonstrate the effectiveness of the proposed approach.