Research on Feature Extraction of Remnant Particles of Aerospace Relays

The existence of remnant particles, which significantly reduce the reliability of relays, is a serious problem for aerospace relays. The traditional method for detecting remnant particles—particle impact noise detection (PIND)—can be used merely to detect the existence of the particle; it is not able to provide any information about the particles’ material. However, information on the material of the particles is very helpful for analyzing the causes of remnants. By analyzing the output acoustic signals from a PIND tester, this paper proposes three feature extraction methods: unit energy average pulse durative time, shape parameter of signal power spectral density (PSD), and pulse linear predictive coding coefficient sequence. These methods allow identified remnants to be classified into four categories based on their material. Furthermore, we prove the validity of this new method by processing PIND signals from actual tests.     

航天继电器多余物颗粒碰撞噪声检测的单周期稳定性分析

航天继电器的可靠性影响着国防电子系统的可靠性,而多余物问题的预防与检测是提高航天继电器可靠性的关键因素。颗粒碰撞噪声检测(PIND)是航天继电器多余物检测的行业规范,目前缺乏对其工作机理的理论研究,其检测效果并不理想。本文从动力学的角度,建立了PIND的数学模型,推导了粒子作单周期等幅振动的必要条件和稳定性条件,并给出了相应的仿真分析。     

航天电磁继电器可靠性容差分析技术的研究

航天电磁继电器是在航天电子系统中完成信号传递、执行控制、系统配电等功能的主要电子元器件之一，其可靠性直接影响整个航天电子系统的可靠性。航天继电器可靠性设计技术是其产品可靠性工程的关键技术。本文分析了当前各领域可靠性研究现状及航天电磁继电器产品的可靠性现状与问题，提出并建立了基于正交试验表的电磁吸力可靠性容差分析数学模型、基于概率统计方法的机械反力可靠性容差分析数学模型及基于“应力-强度干涉法”的吸反力配合特性可靠性容差分析数学模型。以某型号航天电磁继电器为例，进行了可靠性容差分析与研究，给出了对航天电磁继电器可靠性设计具有指导作用的重要结论。     

航天继电器推力杆位置的优化分析

航天继电器的可靠性直接影响航天整机的可靠性，分析并研究航天继电器推动杆的优化位置是进行航天继电器可靠性设计的重要环节。以往人们对航天继电器磁系统、簧片系统做了很多研究工作，但在推动杆位置方面的研究工作很少。提出了一种分析航天继电器推动杆优化位置的方法，即从电磁力、电磁力矩、电磁力做功、位移等四方面，研究直簧系统推动杆的最优位置。研究结论可用于指导航天继电器的参数优化设计、可靠性设计及整体装配调试。     

Asymptotic Performance of Sparse Signal Detection Using Convex Programming Method

The detection of sparse signals against background noise is considered. Detecting signals of such kind is difficult since only a small portion of the signal carries information. Prior knowledge is usually assumed to ease detection. In this paper, we consider the general unknown and arbitrary sparse signal detection problem when no prior knowledge is available. Under a Neyman-Pearson hypothesis-testing framework, a new detection scheme is proposed by combining a generalized likelihood ratio test (GLRT)-like test statistic and convex programming methods which directly exploit sparsity in an underdetermined system of linear equations. We characterize large sample behavior of the proposed method by analyzing its asymptotic performance. Specifically, we give the condition for the Chernoff-consistent detection which shows that the proposed method is very sensitive to the 2 norm energy of the sparse signals. Both the false alarm rate and the miss rate tend to zero at vanishing signal-to-noise ratio (SNR), as long as the signal energy grows at least logarithmically with the problem dimension. Next we give a large deviation analysis to characterize the error exponent for the Neyman-Pearson detection. We derive the oracle error exponent assuming signal knowledge. Then we explicitly derive the error exponent of the proposed scheme and compare it with the oracle exponent. We complement our study with numerical experiments, showing that the proposed method performs in the vicinity of the likelihood ratio test (LRT) method in the finite sample scenario and the error probability degrades exponentially with the number of observations.     

采用超程时间建模的航空航天继电器寿命预测方法的研究

提出以电磁继电器超程时间为变量,运用时间序列回归分析和数理统计分析方法,建立航天密封继电器超程时间减小曲线寿命预测数学模型。预测和实测结果的比较,表明了该方法的有效性。     

Dynamic Analysis of Contact Bounce of Aerospace Relay Based on Finite Difference Method

Contact bounce of relay, which is the main cause of electric abrasion and material erosion, is inevitable. By using the mode expansion form, the dynamic behavior of two different reed systems for aerospace relays is analyzed. The dynamic model uses Euler-Bernoulli beam theory for cantilever beam, in which the driving force (or driving moment) of the electromagnetic system is taken into account, and the contact force between moving contact and stationary contact is simulated by the Kelvin-Voigt viscoelastic contact model. Analytical results have been complemented by the finite difference calculation of the nonlinear partial differential equations of reed system. The calculation results indicate that with the driving force (or driving moment) being increased, the closing time of contact is decreased, but the maximum bouncing displacement and bouncing time tend to be decreased first and then increased, which means that there are minimum values of maximum bouncing displacement and bouncing time. The closing time of contact, the maximum bouncing displacement, and bouncing time all tend to be decreasing with the pushrod being approaching the contact. The model can be used as design tool to improve relay performance and reduce contact bounce.     

二维激光多普勒测速仪在扩散火焰测量中的应用

利用二维激光多普勒测速仪测量了液化石油气-空气扩散火焰的流场,得到了满意的结果。测量采取前向散射接收方式,为此对原后向散射接收光路进行了改造,结果在激光输出功率较低的情况下得到了比较好的多普勒信号。      

基于径向基神经网络干扰观测器的空天飞行器自适应轨迹线性化控制

 研究了一种自适应轨迹线性化控制策略并应用于空天飞行器(ASV)飞行控制系统设计。通过理论分析指明当前轨迹线性化控制方法(TLC)对系统中的不确定存在鲁棒性不足的问题。为了解决这一问题,首先研究了一种径向基神经网络干扰观测器(RDO)技术,严格证明了RDO对于系统中不确定因素具有良好的逼近能力。然后利用RDO输出得到一种新的基于RDO的自适应TLC控制策略。神经网络自适应律采用Lyapunov方法设计,保证了闭环系统所有信号有界。最后采用新方案实现了ASV飞控系统,仿真结果表明整个闭环系统在鲁棒性能方面得到很大提高。     

SiCp/Al复合材料界面控制与评价新方法

尝试了一种控制 Si Cp/Al复合材料界面状态与性能的新途径 ,并首次应用声发射检测技术及其信号的小波分析新方法对控制效果进行了系统地评价。结果表明 :界面控制新方法的采用 ,使 Si Cp/Al复合材料损伤、断裂过程中的 Si C颗粒拔出模式发生了转变 ,进而显著提高了材料的整体力学性能 ;小波分析特别适合于提取此类复合材料断裂所致声发射信号的深层特征 ,并可望成为反映颗粒增强金属基复合材料界面区力学行为、评价其界面性能的有效手段     

小波变换与SVD结合的射频信号消噪方法

射频信号因具备空间远距离传输特性被广泛应用于航天测控等领域,但空间磁场、信道、设备元器件等会引入大量噪声干扰,对射频信号的传播及后续分析处理造成影响。而目前关于射频信号消噪的研究很少,针对此问题,对比研究了传统滤波器消噪、小波阈值法及SVD(Singular Value Decomposition,奇异值分解)法在射频信号消噪方面的应用,仿真分析了各算法对有用射频信号的提取效果,从而发现3种方法均能起到噪声抑制的作用,但是,滤波器法明显会降低信号能量,小波变换法易使重构信号失真,而SVD法则运算时间较长。为此,提出将小波阈值法与SVD相结合,用于射频信号消噪。再通过比对各方法消噪的性能指标,验证了将2种方法结合,可有效提高运算效率及噪声抑制能力。     

非对称衰落信道下无人机中继传输方案及性能分析

 无人机(UAV)作为中继传输平台受到了国内外研究人员的广泛关注。本文研究了非对称衰落信道下的无人机中继传输系统,提出了输出信噪比最大化准则下的波束形成(BF)优化方案,并推导出系统中断概率、遍历容量和平均误符号率等无线通信系统主要性能指标的理论表达式。通过计算机仿真验证了本文提出的中继传输方案及性能分析的正确性,并定量分析了天线数量、信道参数以及功率分配对系统性能的影响,为无人机中继传输系统的设计及性能评估提供了参考和依据。     

基于相关与小波变换相结合的弱信号检测

针对强噪声背景中的弱信号检测问题,在传统的相关检测方法的基础上,利用小波良好的去噪性能,提出了相关检测与小波变换相结合的弱信号检测方法。仿真结果表明,该方法优于传统的相关检测方法,无须知道有用信号的先验知识和同步信号便可以有效的检测周期性弱信号,并获得更高的输出信噪比。     

一种基于QoS的自适应混合协同传输方法及性能分析

理想的混合协同传输系统中,所有的中继节点都参与译码,将带来计算复杂度的提高、节点资源消耗的增加以及信息传输时延的增大。针对多节点的协同无线传输网络,提出了一种基于服务质量(Quality of Service, QoS)的自适应混合协同传输方法,并进行了性能分析,给出了系统误码率(Symbol Error Rate, SER)及中断概率的闭合表达式。该方法根据目的节点的QoS需求以及中继节点的信噪比(SNR)门限,动态地调整中继节点的传输模式,能够减小系统能耗,延长节点使用寿命。数值及仿真结果表明,基于中继节点信噪比门限的混合协同传输能够以较低的实现复杂度获得与理想的混合协同传输相近的性能,理论的性能分析结果与实际仿真结果相一致。     

基于INS/GNSS紧耦合组合的逐步诱导式欺骗检测算法研究

传统惯性辅助GNSS欺骗信号检测方法对小于纯惯导误差漂移的逐步拉偏不敏感。针对这一问题,提出了一种基于INS/GNSS紧耦合组合的逐步诱导式欺骗信号检测方法。基于短时间内纯惯性导航结果和短时间惯导系统位置误差传播模型,利用惯导提供的位置和速度对伪距和伪距率时间序列进行预测估算,并结合伪距、伪距率实际测量结果,分别构造位置/伪距和速度/伪距率时间序列模型做参数拟合。基于真实信号得到的模型参数都是由惯导系统误差造成的,具有一致性;而基于逐步诱导式欺骗信号得到的模型参数还与欺骗策略的设定、载体飞行轨迹的观测以及干扰源的位置有关,具有不一致性。通过对比模型参数可判别欺骗信号的存在,对于真实信号,采用紧耦合组合导航修正INS输出;对于欺骗信号,继续惯性导航并采取相应措施。最后,根据逐步诱导式欺骗原理进行实验仿真,验证了算法的可行性和有效性。     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

Force measurement using strain-gauge balance in shock tunnel based on deep learning

When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.     

宽带信号角跟踪和四通道单脉冲方案

分析了宽带信号角跟踪的特殊问题及其实现方案。基于互相关函数的角误差检测方法,推导出了在非相关噪声及相关噪声背景下差路信号的输出信噪比及其角度随机误差的数学表达式,进而给出了带宽增益和最佳带宽的概念,以及提高输出信噪比的方法。为了在低载噪比（C/N）时获得角捕获所需要的和路信号以及差路的归一化信号,提出了四通道单脉冲方案。最后归纳出了低C/N时宽带信号角捕获和角跟踪的几种方法并提出了建议。     

A type of 2D magnetic equivalent circuit framework of permanent magnet for magnetic system in AEMR

Modeling of permanent magnet(PM) is very important in the process of electromagnetic system calculation of aerospace electromagnetic relay(AEMR). In traditional analytical calculation, PM is often equivalent to a lumped parameter model of one magnetic resistance and one magnetic potential, but great error is often caused for the inner differences of PM; based on the conception of flux tube, a type of 2D magnetic equivalent circuit framework of permanent magnet model(2D MECF) is established; the element is defined, the relationship between elements is deduced, and solution procedure as well as verification condition of this model is given; by a case study of the electromagnetic system of a certain type of AEMR, the electromagnetic system calculation model is established based on 2D MECF and the attractive force at different rotation angles is calculated; the proposed method is compared with the traditional lumped parameter model and finite element method(FEM); for some types of electromagnetic systems with symmetrical structure, 2D MECF proves to be of acceptable accuracy and high calculation speed which fit the requirement of robust design for AEMR.     

Research on Fault-tolerant Control Method of ElectromechanicalActuating System Based on Sliding Mode Observer

机电作动器(Electro-Mechanical Actuator,EMA)正在逐步代替液压/气压作动器而广泛应用于航空航天、军事等领域,特别是在多电/全电飞行器中,机电作动系统已引来大量学者开展各类型研究。在 EMA 的控制方法和实际应用逐渐成熟的前提下,进一步提高系统的可靠性得到广泛的关注。在 EMA 运行过程中能够进行实时的故障检测并保证系统的正常运行,有效提高系统可靠性对于 EMA 的应用具有重要意义。本文提出一种基于滑模观测器的容错控制方法,为 EMA 在工程应用中有效识别系统的运行状态提供重要手段,具有重要的理论意义和工程应用价值。通过仿真验证得出此方法可靠有效,验证了在系统运行过程中能够快速的识别故障并切换控制,表明 EMA 系统在此控制方法下具有很好的故障识别和故障隔离能力。