考虑噪声抑制的高超声速风洞气动力识别方法

对于风洞试验中全尺寸模型试验的非平稳信号进行载荷辨识仍存在诸多问题。针对全尺度模型试验的非平稳信号载荷辨识提出了一种基于深度残差收缩网络（DRSN）深度学习技术的智能载荷辨识方法,该方法通过深度学习提取测力系统输出数据中的气动力、惯性力和噪声等特征,通过注意力机制对每组数据进行获取阈值,再通过软阈值函数对特征进行滤波降噪,有效辨识出测力系统响应信号中的惯性力分量并进行剔除,实现气动力载荷辨识。在测试验证中,均值法的辨识精度为85%以上,DRSN模型的辨识精度为94%以上,证明DRSN模型能有效降低噪声和惯性力对于载荷辨识的干扰,用于非平稳信号的载荷辨识具有精度高、可靠性好等特点。     

Equivalent ARMA model representation for RLG random errors

It is shown that a mixture of random noises can be represented by a single equivalent ARMA (autoregressive moving average) model that is simple to implement. We applied the scheme to model RLG (ring laser gyroscope) random errors. An identification result from real test data confirms the validity of this approach     

Radar Measurement Noise Variance Estimation with Several Targets of Opportunity

A number of methods exist to track a target's uncertain motion through space using inherently inaccurate sensor measurements. A powerful method of adaptive estimation is the interacting multiple model (IMM) estimator. In order to carry out state estimation from the noisy measurements of a sensor, however, the filter should have knowledge of the statistical characteristics of the noise associated with that sensor. The statistical characteristics (accuracies) of real sensors, however, are not always available, in particular for legacy sensors. A method is presented of determining the measurement noise variances of a sensor, assumed to be constant, by using multiple IMM estimators while tracking targets whose motion is not known---targets of opportunity. Combining techniques outlined in [2] and [6], the likelihood functions are obtained for a number of IMM estimators, each with different assumptions on the measurement noise variances. Then a search is carried out over a varying grid of IMMs to bracket the variances of the sensor measurement noises. The end result consists of estimates of the measurement noise variances of the sensor in question.     

激光陀螺误差分析的Allan方差法改进研究

针对传统Allan方差法分析激光陀螺误差特性过程中,不能合理解释方差中部分噪声项出现负值的情况,而采用阻尼振荡的分析方法能够对此给出合理解释,却又损失了辨识精度的问题,提出了一种基于动态Allan方差法理论的改进Allan方差法。该方法将阻尼振荡模型与动态Allan方差模型相结合,使得辨识结果的分析过程更加合理。激光陀螺零偏误差的辨识与分析结果表明:改进Allan方差法的辨识结果精度略高些,在改进Allan方差的三维分析图中可以显示出零偏中存在的数据突变情况,能够反应激光陀螺误差的动态特性。因此,改进Allan方差法更适用于激光陀螺误差特性的分析。     

汽车发电机冷却风扇旋转噪声预测方法

以汽车发电机冷却风扇为对象,针对其修改叶片分布角度前后的旋转噪声预测问题,提出一套较精确且节约计算机时的预测方法.该方法结合了声类比法和矢量合成方法,首先,用大涡模拟(LES)和Ffowcs-Williams和Hawkings(FW-H)方程相结合的声类比方法对原风扇总噪声和主要阶次旋转噪声幅值进行预测;然后,针对只改变叶片分布角度情况下,提出一种矢量合成方法,用于对修改叶片分布前后主要阶次旋转噪声变化量的预测;最后,得到修改后主要阶次旋转噪声的幅值.计算和实验结果表明,原风扇总噪声最大预测误差4.3dB,第12阶和第18阶主要阶次旋转噪声幅值预测误差为1.24dB和4.26dB;修改后风扇第12阶和第18阶主要阶次旋转噪声分别变小了9.3dB和10.5dB,其变化量预测误差分别为0.36dB和0.43dB.结果表明,这一整套对修改前后风扇旋转噪声进行预测的方法是可行的,且大大节省计算时间,为风扇叶片周向分布角度设计提供了很好的依据.      

Estimating Noise Variances by Using Multiple Observers

Unknown variances of the noises that excite a time-invariant, linear dynamic system and/or in the observation of its output can be estimated by use of multiple observers. One observer is needed, in general, for each unknown variance. Each observer is time invariant and has different gains from the others. It is shown that each unknown variance is a linear combination of the variances of the residuals of the observers. The required estimates of the noise variances are obtained by using the measured variances of the residuals. The method presented in this paper is illustrated by an application to determining noise parameters in a ring laser gyro.     

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.     

A recursive multiple model approach to noise identification

Correct knowledge of noise statistics is essential for an estimator or controller to have reliable performance. In practice, however, the noise statistics are unknown or not known perfectly and thus need to be identified. Previous work on noise identification is limited to stationary noise and noise with slowly varying statistics only. An approach is presented here that is valid for nonstationary noise with rapidly or slowly varying statistics as well as stationary noise. This approach is based on the estimation with multiple hybrid system models. As one of the most cost-effective estimation schemes for hybrid system, the interacting multiple model (IMM) algorithm is used in this approach. The IMM algorithm has two desirable properties: it is recursive and has fixed computational requirements per cycle. The proposed approach is evaluated via a number of representative examples by both Monte Carlo simulations and a nonsimulation technique of performance prediction developed by the authors recently. The application of the proposed approach to failure detection is also illustrated     

飞机性能参数预测的不确定性处理

 利用飞机的性能参数对飞机进行故障预报和状态监控是非常重要的。飞机的性能参数不仅具有非线性而且往往包含噪声,使得故障预测结果具有不确定性。针对这些问题,研究了利用非线性支持向量机处理飞机性能参数的预测问题,通过增加线性约束的方式解决了噪声带来的不确定性问题。此种方法不仅提高了预测的精度,而且模型可以利用适用于处理大规模二次规划的序列最小最优化算法进行求解,使得其可以解决大数据量的预测问题。利用仿真数据以及实际飞机性能参数对该方法进行了实验分析,实验结果表明此方法在精度上较不考虑噪声影响的模型有所提高,对于进一步提高飞机故障预测的精度,从而提高飞机的安全性具有重要意义。     

带异步相关噪声的战斗机蛇形机动跟踪算法

针对异步相关噪声背景下战斗机蛇形机动模式转弯角速度辨识问题,考虑到目标状态与转弯角速度之间相互耦合的特性,从联合优化的解决思路出发,基于期望最大化（EM）算法框架,提出了一种带异步相关噪声的联合估计与辨识算法。首先采用"去相关框架"解除过程噪声与量测噪声之间的相关性,从而将异步相关噪声背景下的转弯角速度辨识问题转换成具有一步状态延迟的转弯角速度辨识问题,其次通过解除目标状态与转弯角速度之间的非线性耦合关系,基于期望最大化算法实现了战斗机蛇形机动目标状态与转弯角速度的联合估计与辨识,从而获得转弯角速度闭环形式的解析解：在E-step,通过利用异步相关噪声背景下的高阶容积卡尔曼平滑器（HCKS）,获得目标状态的后验估计;在M-step,通过极大化条件似然函数,获得转弯角速度的解析解。最后通过仿真验证了所提算法的目标状态估计与角速度辨识的精度均优越于传统的扩维法。     

Joint parameter and state estimation for stochastic uncertain system with multivariate skew t noises

Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic gra...     

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

It is difficult to build accurate model for measurement noise covariance in complex back-grounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian (VB) approximation is pro-posed. The variational approximation technique is introduced to the labeled multi-Bernoulli (LMB) filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density (VB-PHD) filter and the VB cardinality balanced multi-target multi-Bernoulli (VB-CBMeMBer) filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.     

时变平均风提取方法研究

非平稳风速模型将实测风速记录分解为时变平均风与平稳脉动风的叠加。研究提出利用脉动风平稳度指标确定时变平均风的方法,定义不同时距摩阻速度的比值为脉动风平稳度指标,在所有可能的选择中,最优时变平均风应使脉动风在满足平稳性要求的同时平稳度指标最大。应用于实测10分钟550组台风及3300组季风数据表明该提取方法合理有效。     

基于降维算法和等效杆长的可展结构精度分析

考虑铰链间隙和杆件尺寸误差的不确定性并通过概率统计方法对其进行研究,提出了一种基于单变量降维算法(UDRM)和等效杆长模型的可展结构精度分析方法。利用UDRM将可展结构的精度性能函数解耦为多个杆件尺寸误差的独立作用形式,建立精度分析模型。引入等效杆长模型,等效杆件替代原杆件进行精度计算。将铰链间隙与原始杆件尺寸误差合并到等效杆件的尺寸误差中,同时证明了等效杆长尺寸误差近似服从正态分布。以某卫星可展开天线为算例,结合高斯求积公式求解展开状态下精度指标的分布期望和方差。通过与蒙特卡罗模拟(MCS)和一次二阶矩(FOSM)法计算结果的对比验证了本文精度分析方法的正确性和高效性。     

基于平滑先验分析和排列熵的滚动轴承故障诊断

由于机械系统的复杂性,滚动轴承振动信号的特征信息表现在不同尺度上,因此需要对振动信号进行多尺度分析。基于此,提出一种基于平滑先验分析(Smoothness priors approach,SPA)和排列熵(Permutation entropy,PE)的滚动轴承故障诊断方法。该方法首先采用平滑先验分析方法代替传统的时间序列分解方法对滚动轴承信号进行分解,得到轴承信号的趋势项和去趋势项;其次,分别计算趋势项和去趋势项的排列熵值;最后,将排列熵值作为特征向量,输入基于粒子群优化支持向量机建立的分类器。将该方法应用于滚动轴承实验数据并进行对比分析,结果表明,在训练样本数为每类50%的条件下,该方法的故障诊断正确率比PE和经验模态分解-PE分别高出12.5%和3.125%。     

随机-有界混合不确定性下结构可靠性优化设计

在结构可靠性分析和设计中,往往无法将所有不确定性参数均作为概率随机变量,存在部分不确定性分布特征不明确而仅知其扰动界限的情况.基于概率模型与多椭球模型的混合不确定性描述,以标准U空间中扩展定义的混合可靠性指标为约束条件,建立了随机不确定性和有界不确定性并存情况下的结构可靠性优化数学模型.利用功能测度方法对原优化模型进行...     

自由振动优化平均和模态隔离

对飞机颤振试飞等运行中的结构或机械在冲击激励下测到的高噪声多模态自由振动,提出一种优化平均方法,可以在大量抑制噪声的同时,充分突出某一感兴趣的模态。此方法基于自由振动频谱与结构频响函数在显示结构振动特性方面的“相似性”,以平均后的自由振动频谱具有最高共振峰作为自由振动平均时“初相角对齐”的优化平均判据。从优化平均自由振动频谱中,又可隔离出有更高信噪比的结构单模态自由振动来,从而使模态参数识别变得十分简单并有高精度。     

Noise immunity of automatic GTE control systems with a selector at channel switching modes

Noise immunity analysis was carried out for different variants of constructing automatic GTE control systems with an algebraic selector at channel switching modes. It is shown that regular or random noises available lead to multiple switching of channels in the transient and steady-state conditions. A method for improving noise immunity is considered, and the results of simulating the algorithms proposed are presented.     

Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts

In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts. The system state, measurement output and filter parameters are all subject to stochastic uncertainties or multiplicative noises, where the measurement noises are finite-step autocorrelated. When there exist multiple packet dropouts in the system output, the original system is converted into an auxiliary stochastic uncertain system by the augmentation of system states and measurements. The process noises and measurement noises of the auxiliary system are shown to be finite-step autocorrelated and cross-correlated. Then, a robust non-fragile Kalman-type recursive filter is designed that is optimal in the minimum-variance sense. The proposed filter is not only robust against the uncertainties in the system model and measurement model, but also non-fragile against the implementation error with the filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed method.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.