飞机垂尾抖振极限载荷预测方法

为满足先进战斗机高速、大机动飞行性能的要求,在飞机设计的强度估算与校核中必须考虑抖振载荷的影响,以保证飞机结构在全包线飞行状态中具有足够的强度。按照飞机飞行状态进行抖振飞行子状态划分建立起抖振载荷数据库的数据存储关系,提出根据有限测点处测试的抖振响应重构飞机垂尾结构抖振载荷的方法,并基于所选抖振飞行子状态载荷概率分布模型预测出抖振极限载荷。最后以某飞机垂尾上某节点的抖振极限载荷预测为例,演示用本文方法预测飞机垂尾抖振极限载荷流程。     

飞机抖振响应数据处理及分析方法

为了编制抖振疲劳谱、估算抖振疲劳寿命等,必须预先对时域的抖振响应数据进行处理与分析。考虑到抖振响应的随机特性,特别是其显著的分散性,建立了统计模型来对其分析处理。针对同一飞行状态下数据仓中的抖振响应数据,将其划分为若干子数据块,以子数据块中数据统计特征来描述对应子数据块均方根下的响应分布情况,而以对应飞行状态下各子数据块的均方根分布情况来描述该状态下抖振响应的总体分布趋势以及选择其关键响应状态水平。首先,采用威布尔分布假设,运用极大似然估计法对子数据块的数据进行分布参数估计,并给出了分布假设的检验方法;然后,采用“三步进”经验函数来描述抖振响应均方根的分布规律。在本文研究的基础上,根据给出的抖振数据处理与分析结果使用流程,即只需根据确定的几个关键均方根水平,定位到相应的子数据块,再结合子数据块数据的统计模型得到对应飞行状态下的响应分布,就可用于飞机设计与强度校核。由实际飞行试验抖振数据的处理与分析表明,此方法具备一定的合理性     

基于统计与粗糙集理论的飞机垂尾抖振载荷分布假设选择与评价方法

研究飞机垂尾抖振问题的主要任务之一是编制抖振疲劳载荷谱、估算飞机结构的抖振疲劳寿命并校核飞机结构的强度等,一般通过统计方法来建立其抖振疲劳载荷时程的峰(谷)值分布模型,而分布模型的优劣对确定各飞行状态下的极限工况及抖振响应的循环次数与幅值分布等信息影响显著。通过分析五种常用于描述抖振疲劳载荷峰(谷)值的概率分布假设模型：正态分布、对数正态分布、威布尔分布、瑞利分布和极值分布,给出了基于参数估计的概率分布规律,并提出了一种采用各概率分布假设所对应模型的“拟合优劣指标”作为评价和选择的依据。同时,结合各分布模型的特性对飞机抖振载荷时程处理要求的匹配程度,运用粗糙集理论确定了系统评价指标的最小分辨距离与最大分辨率,来消除由于误差引入导致的评价指标数值差异而造成的误判。算例分析表明,该方法可合理且高效地实现对飞机抖振载荷概率分布假设的正确评价与选择     

基于最大似然估计的辐射噪声源近场定位方法性能分析

如何利用较少的阵元个数得到比较理想的空间分辨力,准确找到噪声源位置,一直是人们比较关心的问题,因此介绍了基于最大似然估计的辐射噪声源近场定位方法,并利用遗传算法寻求最大似然估计的全局最优解,从而实现噪声源近场定位,其具有比常规聚焦波束形成更高的空间分辨力,且可以有效实现相干声源近场定位。通过计算机仿真详细分析了信噪比、测量距离及基阵孔径对本文算法定位性能的影响,说明了仅利用小孔径基阵就可实现辐射噪声源近场高分辨定位,最后通过湖试实验验证了该方法的有效性,具有一定的工程应用价值。     

基于AMLE的高光谱本征维数估计方法研究

最大似然估计法（MLE）能快速计算高光谱影像数据的本征维数,原理简单,但它不是一种很好的本征维数估计法。因为它存在忽略单个数据点贡献的缺点,导致冗余信息放大、重要信息被湮没,不利于特征提取和分类。对此．本文引入自适应性最大似然估计法（AMLE）。该方法通过引入权函数,突出每个数据点的贡献,能提取出被最大似然估计法湮没的重要特征。在对高光谱影像进行特征提取及分类的试验中验证了该方法的有效性。     

基于Hilbert-Huang变换的大跨桥梁非线性抖振响应时频分析

以新疆赛吾迭格尔悬索桥为工程背景,研究非线性抖振响应的时频特性。提出了Fourier变换识别桥梁抖振响应的频谱的失真问题,从信号分析的角度寻找失真的原因,从结构动力学角度探讨响应非平稳性的导因。为解决这个问题,引入Hilbert-Huang变换(HHT变换)分析响应的边际谱。并分析HHT变换中经验模态分量的时频,从风工程角度探讨经验模态分量的物理意义。分析结果表明,大跨桥梁的颤抖振响应具有一定的非平稳性,HHT变换识别抖振响应的频谱时比Fourier变换精度更高。     

基于差分进化的时变信道最大似然估计算法

研究了隧道环境下的通信信道估计。针对隧道环境的地铁列车与轨旁设备之间无线通信中无线传输信道快速变化的特点,提出了一种采用元胞差分进化(DE)方法实时获取时变信道的有效信道长度的新型最大似然(ML)信道估计算法——DE-ML算法。仿真结果表明该算法在使用较少导频信息的情况下,通过差分进化方法有效估计跟踪有效信道长度,其估计性能优于最小二乘(LS)、线性最小均方误差(LMMSE)、传统ML等经典信道估计算法。该算法能在提高系统传输效率的同时显著提高算法的估计精度,尤其在高速移动情况下也具有了非常良好的性能。     

一种基于充气气囊的垂尾抖振抑制新方法研究

通过数值模拟探索了一种运用充气气囊抑制双垂尾抖振的新方法。该文方法利用充气气囊可迅速充气变形的特点,在三角翼上翼面靠近顶点沿涡核的位置设置气囊。在小迎角下气囊不凸起,从而保证机翼前缘涡的强度以产生非线性涡升力;当大迎角抖振现象较严重时,迅速对气囊充气形成凸起,该凸起通过对前缘分离涡的强度和涡空间位置的影响,减弱涡破裂对双垂尾的非定常气动载荷激励,达到抑制抖振的目的。对某三角翼双垂尾布局模型的计算结果表明：气囊可以使前缘涡的涡核弯曲、扭转,减弱了前缘涡的强度,使前缘涡破裂点位置提前,在大迎角范围可将垂尾绕翼根的弯矩值显著减小,并且减小了垂尾表面压力脉动的幅度和对应的功率谱密度的峰值。因此,该文所探索的利用充气气囊抑制抖振的方法是一种简单可靠,并且值得进一步研究的技术途径。     

基于EMD间隔阈值消噪与极大似然估计的滚动轴承故障诊断方法

针对小波阈值消噪用作滚动轴承故障信号处理存在小波基函数较难选择及传统硬阈值、软阈值消噪效果差等缺点,将EMD间隔阈值消噪与极大似然估计相结合,应用于滚动轴承早期微弱故障诊断。对原始信号进行EMD分解;对各IMF进行基于极大似然估计的间隔阈值消噪,并重构出故障信号;进行包络谱分析得出诊断结果。数字仿真信号与实验信号验证了该方法的有效性。     

扰流激励下垂尾抖振响应主模态控制风洞试验研究

摘 要：采用压电结构的热弹比拟建模方法,进行了垂尾模型一弯模态和一扭模态响应的压电主动控制仿真。设计制作了一个垂尾气动弹性抖振模型以及两种形式的气流干扰源,用于在风洞中进行垂尾抖振实验及产生扰流对垂尾模型实施抖振激励。采用自主研发的弓形压电作动器,根据垂尾抖振响应控制的主模态控制思想,设计了垂尾模型抖振压电主动控制系统,进行了垂尾模型抖振响应压电主动控制风洞实验。结果表明,采用抖振主模态响应控制思想设计的垂尾抖振压电主动控制系统,可使垂尾模型抖振响应功率谱密度函数峰值降低50%以上。     

A novel pseudo‐maximum likelihood estimation method for accelerated degradation tests under Wiener process

For reliability assessment based on accelerated degradation tests (ADTs), an appropriate parameter estimation method is very important because it affects the extrapolation and prediction accuracy. The well‐adopted maximum likelihood estimation (MLE) method focuses on interpolation fitting and obtains results via maximizing the likelihood of the observations. However, a best interpolation fitting does not necessarily yield a best extrapolation. In this paper, therefore, a pseudo‐MLE (P‐MLE) method is proposed to improve the prediction accuracy of constant‐stress ADTs by considering the degradation mechanism equivalence under Wiener process. In particular, the degradation mechanism equivalence is characterized by a mechanism equivalence factor which presents the proportional relationship between degradation rate and variation. Then, the mechanism equivalence factor is determined via a two‐step method. The other model parameters can be estimated by the general MLE method. The asymptotic variances of acceleration factors and the p‐quantile of product failure time under normal condition are adopted to compare the statistical properties of the proposed method and the general MLE approach. Numerical examples show that the novel P‐MLE method may not achieve a maximum likelihood but can provide more benefits regarding prediction accuracy enhancement especially when the sample size is limited.     

The extreme value theory approach to safety estimation

Crash-based safety analysis is hampered by several shortcomings, such as randomness and rarity of crash occurrences, lack of timeliness, and inconsistency in crash reporting. Safety analysis based on observable traffic characteristics more frequent than crashes is one promising alternative. In this research, we proposed a novel application of the extreme value theory to estimate safety. The method is considered proactive in that it no longer requires historical crash data for the model calibration. We evaluated the proposed method by applying it to right-angle collisions at signalized intersections. Evaluation results indicated a promising relationship between safety estimates and historical crash data. Crash estimates at seven out of twelve sites remained within the range of Poisson-based confidence intervals established using historical crash data. The test has yielded large-variance safety estimates due to the short 8-h observation period. A simulation experiment conducted in this study revealed that 3-6 weeks of observation are needed to obtain safety estimates with confidence intervals comparable to those being obtained from 4-year observed crash counts. The proposed method can be applied to other types of locations and collisions as well.     

Parameter estimation of the generalized extreme value distribution for structural health monitoring

Structural health monitoring (SHM) can be defined as a statistical pattern recognition problem which necessitates establishing a decision boundary for damage identification. In general, data points associated with damage manifest themselves near the tail of a baseline data distribution, which is obtained from a healthy state of a structure. Because damage diagnosis is concerned with outliers potentially associated with damage, improper modeling of the tail distribution may impair the performance of SHM by misclassifying a condition state of the structure. This paper attempts to address the issue of establishing a decision boundary based on extreme value statistics (EVS) so that the extreme values associated with the tail distribution can be properly modeled. The generalized extreme value distribution (GEV) is adopted to model the extreme values. A theoretical framework and a parameter estimation technique are developed to automatically estimate model parameters of the GEV. The validity of the proposed method is demonstrated through numerically simulated data, previously published real sample data sets, and experimental data obtained from the damage detection study in a composite plate.     

基于MLE阈值规则的小波特征提取技术在气阀故障诊断中的应用

气阀故障是往复压缩机最常见的故障类型之一,占故障总数的60%以上,如果不及时发现并解决,往复压缩机的压缩效率将大大降低。针对目前往复压缩机气阀故障诊断中存在的问题,结合小波降噪技术,提出了采用基于最大似然估计（MLE：Maximum Likelihood Estimation）阈值规则对气阀早期故障弱冲击变化信号进行特征提取的方法,实现了气阀故障的早期预警。     

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes’ theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito–Taylor’s discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplified by considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations.     

Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model

Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application.     

Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel

Proper definition of certain material properties is a paramount issue for accurate simulation. However, the values of a material parameter are commonly uncertain due to multiple factors in practice. To obtain reliable material parameters, parameter identification via Bayesian theory has become an attractive framework and received more attention recently. Based on this frame, the determination of likelihood function is critical for posterior probability. Unfortunately, it is commonly difficult to be determined directly, especially for complex engineering problems. In this study, Bayesian formulas for material parameter identification are given. To make it feasible for real engineering problems, the least square-support vector regression surrogate and Monte Carlo Simulation are integrated to obtain the maximum likelihood estimation of likelihood function. The uncertainty of parameter identification is quantified via the Bayesian method. In two benchmarks, two cases with single and multiple uncertainty sources are used to propagate and quantify uncertainties in material parameters based on Bayesian approach. Moreover, the proposed method is used to identify the material parameters of advanced high strength steel used in vehicle successfully.     

On a new extension of Weibull distribution: Properties,estimation, and applications to one and two causes of failures

In this paper, we try to supplement the distribution theory literature by introducing a new distribution, called the logarithmic transformed Weibull (LTW) distribution by logarithm transformation method. The proposed distribution exhibits constant, decreasing, increasing, unimodal and unimodal then bathtub-shaped hazard rates. Although our main focus is on the estimation from the frequentist point of view, in addition, we derive some useful structural and statistical properties of the proposed LTW distribution. The LTW parameters are estimated by eight frequentist methods of estimation. We perform extensive simulation experiments to show the performances of the proposed estimators. Applications of the model are presented by reanalyzing two real data sets, and comparisons are made with the fit attained by some other well-known distributions for illustrative purposes. As an illustration, one data set is analyzed for competing risks to demonstrate the flexibility of the proposed model.