线性/非线性时间序列模型在推力轴承油膜厚度测量中的应用

通过对推力轴承油膜厚度特征量的采集、提取。利用GNAR模型对数据进行拟合并和实际测量值进行比对,较小的误差验证了非线性时间序列模型GNAR适用于轴承油膜厚度的状态识别和动态预测,并在时序图上发现轴承油膜厚度与轴承转速和轴承所受载荷存在密切的关系。     

基于贝叶斯推断贡献的机械设备故障诊断方法

现有机械设备故障诊断方法大多针对已知故障建立,不具备识别新故障的能力,针对该问题提出一种基于贝叶斯推断贡献(Bayesian Inference Contribution,BIC)的故障诊断方法.该方法基于BIC理论对各已知故障建立状态模型,并自学习振动数据与各状态模型的允许最大距离,当振动数据与各已知故障状态模型间距离均大于允许最大距离时识别为新故障,反之为已知故障,并在此基础上设计了状态模型更新方法,使其能够利用新故障数据和误诊数据对模型进行更新.采用往复压缩机的真实故障数据进行测试,结果表明,该方法对新故障的识别准确率为100％,采用误诊数据对状态模型更新后整体诊断准确率得到明显提升.     

基于全局与局部特征结合的抽油机故障诊断

为了准确及时地判断抽油机故障位置，改变低效的人工故障诊断模式，提出了一种基于马田系统的抽油机故障识别方法。首先，分析示功图的几何特征和形状特征；其次，利用全局与局部特征相结合的方式，对示功图进行特征提取；然后，以马氏距离作为度量指标，通过构建和修正特征子集，筛选出各故障的敏感特征变量；最后，建立多分类马田系统实现故障识别，并与传统人工识别方法对比，以克拉玛依石西作业区的油井数据为例，验证方法的可行性和有效性。     

机械故障诊断学的理论及其应用——第三讲 信号的特征分析与特征量选择

信号的特征分析是动用各种现代科学方法对观测数据进行分析研究,寻找各特征量的变化规律;然后选择对工况状态最敏感的特征量以诊断故障,即所谓的去伪存真、去粗取精.本文主要介绍了时域、频域以及新近发展的时频域分析方法.     

基于高阶累积量的机械故障诊断方法研究

利用高阶统计量可抑制高斯噪声的特性,文中构造了滞后量为变量的三阶和四阶累积量特征诊断分类指标,对滚动轴承4种不同状态(正常、外圈剥落、内圈剥落、滚动体剥落)进行了识别,结果表明:正常和故障信号的高阶累积量特征指标在图形上存在明显差别,可用于故障分类。结合欧氏距离进行状态定量识别,取得了很好的诊断效果。     

基于PCA-SVM集成阀门故障诊断方法研究

提出了一种基于主元分析和支持向量多分类器的故障诊断方法.该方法首先对工业故障数据进行主元分析提取数据集特征并降低数据维数,再把故障特征数据通过支持向量多分类器进行模式分类,最后通过特征分类诊断故障.在DAMADICS阀门模型上进行了仿真,并利用Lublin Sugar Factory工业故障数据进行了验证.仿真结果表明该方法可以快速准确地检测与诊断故障.     

基于混合模型与改进多分类马田系统的控制图模式识别

为提高生产过程中产品质量的智能监控水平,提出基于时间序列混合模型及改进多分类马田系统的控制图模式识别算法。选用时间序列混合模型对控制图实时数据进行特征提取;改进马田系统的阈值计算方法并制定多类判别准则,将表征的特征向量代入改进多分类马田系统分类器中进行特征约减及模式识别。最后,将该识别算法应用于控制图公开数据集及生产案例中,以验证算法的有效性,并与其他算法对比了分析,结果表明,基于时间序列混合模型及改进多分类马田系统算法能简化识别系统,识别精度高,是一种更为有效的控制图模式识别方法。     

基于双维度EKNN的滚动轴承早期故障分类算法

为提高对滚动轴承早期故障识别的精确度,提出一种基于混合特征提取的故障分类模型.该模型利用类内紧致性和类间重叠性识别出次优特征组,作为增强K近邻分类器(EK N N)的输入,并以距离和密度双维度计算,得到最大平均分类精确度,进而输出最优早期故障特征组,对未知数据进行分类来检测故障.实验采集滚动轴承在低速运行下的早期故障声发射(A E)信号,对所提算法以及现有5种算法进行对比分析,验证了其对滚动轴承早期故障诊断具有更好的表现.     

以输入电流谐波为特征量的SOM网络整流电路故障诊断方法

针对故障诊断系统中最重要的故障特征提取和故障识别环节,提出了基于输入电流谐波变化,利用人工神经网络对整流电路进行故障诊断的方法。以三相桥式全控整流器为模型电路,以各类故障状态下的三相交流侧输入电流谐波数据为故障特征,选取自组织特征映射神经网络(SOM)为故障识别算法,对得到的谐波数据进行编程分析识别,通过多次学习和训练,对各类元器件故障进行精确分类,从而建立起故障诊断数据库和查询途径,完成对各类故障的检测。该算法训练速度快、诊断精度高,通过仿真和相关实验验证了理论分析的可行性和有效性。     

核函数主元分析及其在齿轮故障诊断中的应用

提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与核函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。     

General expression for linear and nonlinear time series models

The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.     

不同轴承支撑下碰摩转子系统的动力学特性

研究了不同类型轴承支撑下的转子碰摩故障振动特征。首先,采用了计及回转效应、剪切效应及横向扭转的梁单元建立了实验装置转子的有限元分析模型;然后,通过二维计算流体力学方法得到不同类型轴承的动力学参数;最后,在此基础上,结合非线性动力学理论,分析比较了转子在圆柱轴承、可倾瓦轴承等5种轴承支撑下的碰摩非线性动力学行为。研究结果表明：在椭圆轴承、五瓦轴承和四瓦可倾瓦轴承支撑下,系统主要表现为同频周期运动;而对于圆柱轴承和四油叶轴承支撑,系统会出现较复杂的运动。本研究工作是对之前转子碰摩非线性动力学研究的扩展,所得计算结果可为大型高速旋转机械系统动态设计制造和碰摩故障的诊断和控制提供理论参考。     

基于半物理仿真技术的机电伺服系统模型辨识研究

系统模型是进行系统性能分析与设计的基础。为了获得准确的系统数学模型，文章借助于dSPACE实时系统的半物理仿真环境和MATLAB系统辨识工具箱，提出了一种适用于机电伺服系统的模型辨识方法，并以机器人关节伺服系统为对象进行了系统模型辨识实验研究，通过对比离线仿真和半物理仿真结果验证了该方法的有效性。该研究对机电系统建模及控制系统设计具有参考价值。     

基于RBF神经网络的非线性系统智能控制

针对工业控制领域中复杂非线性时变系统，采用传统的控制方法不能达到满意的控制效果，提出了基于神经网络的PID自适应控制方案。采用神经网络辨识器在线辨识系统模型，自动调整PID控制器参数，从而实现系统的智能控制。仿真结果表明该方法对于复杂非线性系统能进行有效的控制并且具有很好的自适应性和鲁棒性。     

用FCGA优化的四辊冷轧机恒张力模糊控制系统

为了保证冷轧机轧制中带钢恒张这一特点，设计了四辊冷轧机恒张力模糊控制系统。该系统含有电压、电流和速度三个内环，最外环为张力环；电压环和电流环采用一维模糊控制器控制，速度环和张力环采用二维模糊控制器控制。为了加强模糊控制的自适应性，采用了一种模糊控制的遗传算法将外张力模糊控制器的隶属参数进行优化。理论分析和仿真结果都表明该系统对带钢恒张控制具有很强的鲁棒性和实时性，即使在变工况下（大范围变负荷下）也保持了良好的控制性能。     

不对称润滑对碰摩转子-轴承系统的动力学影响

在考虑非线性油膜力的基础上,建立了具有碰摩故障的转子-轴承系统的动力学模型。用数值方法研究了在非线性油膜力作用下具有碰摩故障的转子系统的动力学特性,并研究了当改变其中一个支承轴承润滑油的粘度时,转子系统的动力学特性。研究发现,随着一轴承润滑油粘度的降低,转子系统亚临界角速度区的混沌区域和拟周期区域扩大了。该结果为采用不同粘度润滑剂的转子-轴承系统的优化设计、安全运行和故障诊断等提供了一定的理论参考     

Analytical approach to robust design of nonlinear mechanical systems

The robustness of mechanical systems is influenced by various factors. Their effects must be understood for designing robust systems. This paper proposes a model for describing the relationships among functional requirements, structural characteristics, design parameters and uncontrollable variables of nonlinear systems. With this model, the sensitivity of systems was analyzed to formulate a system sensitivity index and robust sensitivity matrix to determine the importance of the factors in relation to the robustness of systems. Based on the robust design principle, an optimization model was developed. Combining this optimization model and the Taguchi method for robust design, an analysis was carried out to reveal the characteristics of the systems. For a nonlinear mechanical system, relationships among structural characteristics of the system, design parameters, and uncontrollable variables can be formulated as a mathematical function. The characteristics of the system determine how design parameters affect the functional requirements of the system. Consequently, they affect the distribution of system performance functions. Nonlinearity of the system can facilitate the selection of design parameters to achieve the required functional requirements.     

Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach

This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.     

优化的精度控制自适应调节方案

针对非线性不确定系统,研究并优化了基于输入信号参考自适应PID控制方法——在模型参考自适应PID控制的基础上以输入信号为参考代替模型参考对系统进行控制。控制方法是基于李雅普诺夫平衡点稳定定理控制方法的系统稳定性推演的,由于控制方法中引入稳定项后使控制过程带来微分项,控制方法中加入滤波器,通过系统的自适应调节最终使系统趋于稳定状态。同时,在控制中加入PID因子使系统的控制性能趋佳。该方法是基于输入信号为参考的自适应控制,减少了模型参考带来的误差。仿真推演结果显示,被控对象可以有较强的适应能力,趋于实现更优的过程控制效果。文中给出了运用于海洋钻井设备运动补偿装置实例的分析。     

Characteristic verification and parameter optimization of airbags cushion system for airborne vehicle

Abstract： The major methods to investigate the airbags cushion system are experimental method, thermodynamic method and finite element method （FEM）. Airbags cushion systems are very complicated and very difficult to be investigated thoroughly by such methods For experimental method, it is nearly impossible to completely analyze and optimize the cushion characteristics of airbags of airborne vehicle because of charge issue, safety concern and time constraint. Thermodynamic method fails to take the non-linear effects of large airbag deformation and varied contact conditions into consideration. For finite element method, the FE model is usually complicated and the calculation takes tens of hours of CPU time. As a result, the optimization of the design based on a nonlinear model is very difficult by traditional iterative approach method. In this paper, a model based on FEM and control volume method is proposed to simulate landing cushion process of airborne vehicle with airbags cushion system in order to analyze and optimize the parameters in airbags cushion system. At first, the performance of airbags cushion system model is verified experimentally. In airdrop test, accelerometers are fixed in 4 test points distributed over engine mount, top, bottom and side armor plate of hull to obtain acceleration curves with time. The simulation results are obtained under the same conditions of the airdrop test and the simulation results agree very well with the experimental results, which indicate the established model is valid for further optimization. To optimize the parameters of airbags, equivalent response model based on Latin Hypercube DOE and radial basis function is employed instead of the complex finite element model. Then the optimal results based on equivalent response model are obtained using simulated annealing algorithm. After optimization, the maximal acceleration of airborne vehicle landing reduces 19.83%, while the energy absorption by airbags increases 7.85%. The performance of the airbags cushion system thus is largely improved through optimization, which indicates the proposed method has the capability of solving the parameter optimization problem of airbags cushion system for airborne vehicle.