小波—AR谱估计在齿轮故障诊断中的应用

针对在齿轮箱故障诊断中,由于现场环境的噪声干扰,采集的故障信号的信噪比低.用FFT变换求功率谱进行谱分析时,转频、啮合频率被很强的背景噪声所淹没,无法判别齿轮是否有故障.在采用峭度、偏态等统计量特征值初步判别齿轮是否异常的基础上,用小波降噪方法提取故障信号,并用AR模型进行谱估计,确定齿轮的故障类型及严重程度.     

基于鲁棒H∞滤波的锂离子电池SOC估计

荷电状态（State of charge, SOC）估计是电池管理系统的核心功能之一,它在电动汽车的生命周期中起着重要作用。针对锂离子电池温度影响模型参数,进而导致SOC估计不准确的问题,本文提出了基于鲁棒H_∞滤波的SOC估计方法。首先,以二阶Thevenin等效电路模型做为锂离子电池基础模型,并将温度对电池模型参数的影响建模为标称电阻值和电池总容量的加性变量,视温度变化为系统的外部扰动。其次,采用滑动线性法对电池模型进行线性化,并在此基础上运用线性矩阵不等式技术设计了对SOC进行估计的鲁棒H_∞滤波器。最后,分别采用四种不同类型的动态电流激励进行仿真实验验证,并将SOC的估计结果与kalman滤波对SOC的估计结果进行对比。结果表明所设计的鲁棒H_∞滤波器能够实现对SOC更为准确的跟踪,同时对外部扰动具有较好的鲁棒性。      

主元分析的振动频域特征识别与磨机负荷建模研究

球磨机是矿石破磨的关键设备,由于运行中筒体内负荷(ML)难以有效检测,运行状态参数的预报控制无法进行。研究通过检测筒体轴承座产生的振动信号,采用Welch法对其进行功率谱估计,提取信号的特征频谱段的能量值,分析信号功率谱与磨机筒体内负荷之间的关系,采用主元分析法(PCA)对振动谱能量值进行降维,得到与负荷高度相关的能量谱成分,最后利用支持向量机(SVM)建立磨机负荷参数分类模型,实现磨机负荷参数(填充率、料球比)的预测。试验结果表明:该方法可以准确识别磨机筒体内负荷,为状态参数的预报及控制技术的优化提供理论指导。     

锡矿井下光纤光栅温度测量用掺铒光纤光源研究

本文分析了铒纤长度、泵浦功率对ASE光谱宽度及平坦度的影响,通过模拟得到10m最佳光纤长度及100mW的最佳泵浦功率,进一步设计了增益平坦滤波器损耗参数,根据设计结果搭建光路,得到较为平坦的ASE光源输出,且平坦度达到了±1.5dB,接入频率800Hz可调滤波器,获得1526-1565nm完整的光谱可调信号,完成了可调掺铒光纤光源的研究,得到锡矿井下开采所需光纤光栅温度测试仪的宽带光源。     

基于AR模型的磨机振动信号特征提取方法

磨矿过程中的球磨机筒体内部负荷(填充率、料球比)研究是选矿设备节能降耗的重要内容。以试验球磨机为对象,通过采集轴承座振动信号,采用AR模型对振动信号进行特征提取和进行功率谱估计,研究了5种充填率条件下的磨机负荷参数与信号时域特征的相关性,得出随着磨矿过程中筒体中钢球、物料的变化,低频段、高频段的频谱能量值曲线的相应变化规律。研究表明,有量纲时域特征参数可以很好地表征特征信号与负荷状态参数的相关性;无量纲时域特征参数可以很好地解释振动时域信号的波形分布以及振动冲击特征,根据频谱能量分布变化规律,可提取能够表征其对应磨机负荷状态的振动特征,为磨机负荷预测提供依据。     

CNN-PDE非线性图像滤波器

偏微分(PDE)非线性图像滤波方法具有优良特性,但由于其计算量大而无法满足实时控制需求.细胞神经网(CNN)可以描述图像PDE模型,利用模拟CNN芯片并行求解,有助于提高其实时性.本文用CNN实现了PDE偏差非线性图像滤波器,提出了一种局部运算的噪声估计方法以选择适当的平滑系数.计算结果表明,这种噪声估计方法可以对不同噪声水平作出较精确的估计.仿真实验结果表明,CNN-PDE非线性滤波器取得了满意的滤波效果,用CNN实现PDE非线性滤波器的方法是有效可行的.     

基于机理和数据驱动的转炉输入—输出混合模型

为了实现能量流网络的精细化控制,建立了基于机理和数据驱动的转炉输入-输出模型.对转炉工序进行物质的输入和输出解析,根据实际生产数据,利用数理统计和回归的方法,得到转炉冶炼相关参数,包括:氧气利用率、炉渣碱度、渣中氧化镁含量、钢水终点氧含量、转炉热效率.进而利用冶炼机理以转炉冶炼的铁水和废钢数据,以及目标钢水的成分和温度为输入量,计算得到吹氧量、造渣剂加入等信息作为模型的输出量.根据机理模型计算的部分输出参数,利用神经网络预测钢水终点温度,并与机理模型采用的目标钢水温度进行对比,进而对机理模型进行校正,以提高模型的精确度.采用C#语言将模型程序化,模型计算结果表明,相同误差范围内,混合模型的石灰加入量、轻烧白云石加入量、氧化球团加入量命中率相较于机理模型分别提高了11.1 %、8.3 %、8.3 %.      

卡尔曼滤波器在钢板运动自动跟踪中的应用

从实际出发,以昆明钢铁集团公司中板厂钢板运动为研究对象,在系列帧图像中对运动目标以直方图为模型的模板方法进行匹配,由于模板匹配计算量非常大,要想在整幅图像中对目标进行搜索匹配又要达到实时是不可能的,对目标状态进行可靠的估计,就可以在相对较小的区域完成对模板的搜索,Kalman滤波器就是一个对动态系统的状态序列进行线形最小方差估计的算法,通过以动态的状态方程和观测方程来描述系统,它可以任意一点作为起点开始观测,采用递归滤波的方法计算,它具有计算量小,可实时计算的特点．     

随机-模糊线性回归模型的参数估计及应用

导出了随机-模糊线性回归模型参数的估计量,证明了参数的估计量为无偏估计,同时推导了参数估计量数字特征和回归方程相关系数的计算公式。将该模型应用于岩石样本抗剪强度实验数据处理中,通过与传统的随机一元线性回归对比分析,表明使用该方法得到的力学参数更具代表性。     

MnO-SiO2二元系SELF-SReM模型及应用

介绍了用于计算MnO-SiO2二元均相系中组元活度的高阶亚正规溶液(SELF-SReM)模型.成功地引入了贝叶斯统计法来估计该模型参数,并对其结果进行相关性和显著性分析,证明了估计结果的可靠性.用该模型计算了MnO-SiO2二元系各组元的活度,计算结果与他人的研究结果较一致,并用该模型控制Si-Mn合金脱氧的氧浓度及夹杂物成分,计算结果对脱氧氧浓度及夹杂物控制有一定的指导意义.     

Nonstationary Random Critical Excitation for Acceleration Response

The critical excitation method is promising as a robust method for accounting for inherent uncertainties in predicting forthcoming earthquake events and for constructing design earthquake ground motions in a reasonable way. Most of the proposed theories are based on deterministic approaches and deal with displacement responses. A stochastic acceleration response index is treated here as the objective function to be maximized. The power (area of power spectral density function) and the intensity (magnitude of power spectral density function) are fixed and the critical excitation is found under these restrictions. It is shown that the original idea for stationary random inputs can be utilized effectively in the procedure for finding a critical excitation for nonstationary acceleration responses of nonproportionally damped structural systems. Several numerical examples are presented to demonstrate the characteristics of generalized time-varying frequency response functions for models with various stiffness and damping distributions.     

Amplitude Variability in Simulated Incoherent Seismic Ground Motions

This note compares in detail four commonly used schemes for the simulation of spatially variable ground motions. Emphasis is placed not only on the conformity of the simulations with the power and cross spectral density of the random field but, also, on the examination of the consistency of the simulations with the homogeneity condition, and the (Fourier) amplitude variability of the simulations. It is shown that, whereas three techniques that simulate ground motions in parallel satisfy the homogeneity requirement, produce simulations with random amplitudes, and amplitude and phase variability consistent with that of recorded data, one technique that simulates motions in sequence does not.     

Stochastic Finite-Element Analysis for Elastic Buckling of Stiffened Panels

The variability of the random buckling loads of beams and plates with stochastically varying material and geometric properties is studied in this paper using the concept of the variability response function. The elastic modulus, moment of inertia, and thickness are assumed to be described by homogeneous stochastic fields. The variance of the buckling load is expressed as the integral of the auto- and cross-spectral density functions characterizing the stochastic fields multiplied by the deterministic variability response functions. Using this expression spectral-distribution-free upper bounds of the buckling load variability are established. Further, the buckling load variability for prescribed forms of the spectral density functions is calculated. Using a local average approach, the commercial finite-element package ABAQUS is incorporated into the analysis of these random buckling loads. The technique is applied to study variability of the critical buckling load of a stiffened steel plate used in experiments to model a barge deck.     

Stochastic Spatial Excitation Induced by a Distributed Contact on Homogenous Gaussian Random Fields

The contact between vehicle tire and pavement surface random field is typically modeled as a point contact in the literature of vehicle-pavement interaction. In reality, tire-pavement interface can be considerably larger than a point contact, particularly when a tire is not very stiff and pavements are relatively soft. This paper developed a methodological framework that approximately aggregates one- and two-dimensional random fields within the contact area by taking local, weighted spatial average to account for the distributed contact. Statistical properties such as power spectral density, autocorrelation function and variance of the induced spatial excitation are related to the counterparts of the original random field. It was found that the distributed contact acts like a low-pass filter whose bandwidth is governed by the contact interface and the weight function.     

Polynomial Chaos Decomposition for the Simulation of Non-Gaussian Nonstationary Stochastic Processes

A method is developed for representing and synthesizing random processes that have been specified by their two-point correlation function and their nonstationary marginal probability density functions. The target process is represented as a polynomial transformation of an appropriate Gaussian process. The target correlation structure is decomposed according to the Karhunen–Loève expansion of the underlying Gaussian process. A sequence of polynomial transformations in this process is then used to match the one-point marginal probability density functions. The method results in a representation of a stochastic process that is particularly well suited for implementation with the spectral stochastic finite element method as well as for general purpose simulation of realizations of these processes.     

Tuned Mass Damper Design for Optimally Minimizing Fatigue Damage

Traditionally the usage of a tuned mass damper (TMD) is to improve the survivability of the primary structure under an extraordinary loading environment while the design loading condition is often described by a harmonic function, or sometimes by a stationary random process that can be fully characterized by a power spectral density (PSD) function. In contrast, this paper considers the environmental loading to be a long-term nonstationary stochastic process characterized by a probabilistic PSD function. One engineering motivation to design a TMD under a long-term random loading condition is for prolonging the fatigue life of the primary structure. The primary contribution of this study is to provide the theoretical framework for designing a TMD that can optimally minimize structural fatigue damage.     

Conditional Simulation of a Class of Nonstationary Space-Time Random Fields

This paper first addresses the problem of conditionally simulating stationary, space-time, Gaussian random fields. A method developed for quadrant-symmetric, stationary, space-time fields is extended to account for the imaginary part of the complex cross-spectrum. The case of amplitude- and frequency-modulated (AFM) nonstationary space-time fields is studied next. The evolutionary spectral density and cross-correlation structure of such a class of nonstationary random fields are analyzed in terms of the envelope and frequency modulation functions. A method for the conditional simulation of AFM space-time fields is advanced. The AFM nonstationary fields are mapped to a domain where the conditional simulation is performed as for stationary, space-time fields. Examples are given to illustrate the use and capabilities of the method including applications to the simulation of earthquake ground motions.     

Stochastic Finite Elements with Multiple Random Non-Gaussian Properties

The spectral formulation of the stochastic finite-element method is applied to the problem of heat conduction in a random medium. Specifically, the conductivity of the medium, as well as its heat capacity are treated as uncorrelated random processes with spatial random fluctuations. Using the spectral stochastic finite-element method, this paper analyzes the sensitivity of heat conduction problems to probabilistic models of random data. In particular, both the thermal conductivity and the heat capacity of the medium are assumed to be uncertain. The implementation of the method is demonstrated for both Gaussian and lognormal material properties, modeled either as random variables or random processes.     

Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. We have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). We conclude that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals we have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.     

Proper Orthogonal Decomposition-Based Modeling, Analysis, and Simulation of Dynamic Wind Load Effects on Structures

Multicorrelated stationary random processes/fields can be decomposed into a set of subprocesses by diagonalizing their covariance or cross power spectral density (XPSD) matrices through the eigenvector/modal decomposition. This proper orthogonal decomposition (POD) technique offers physically meaningful insight into the process as each eigenmode may be characterized on the basis of its spatial distribution. It also facilitates characterization and compression of a large number of multicorrelated random processes by ignoring some of the higher eigenmodes associated with smaller eigenvalues. In this paper, the theoretical background of the POD technique based on the decomposition of the covariance and XPSD matrices is presented. A physically meaningful linkage between the wind loads and the attendant background and resonant response of structures in the POD framework is established. This helps in better understanding how structures respond to the spatiotemporally varying dynamic loads. Utilizing the POD-based modal representation, schemes for simulation and state-space modeling of random fields are presented. Finally, the accuracy and effectiveness of the reduced-order modeling in representing local and global wind loads and their effects on a wind-excited building are investigated.