基于任务弧段的测控通信任务可靠性建模方法

基于对任务的弧段划分,建立了测控通信任务的可靠性模型.根据任务执行时序图,参与任务的测控通信资源及任务成败标准,将测控通信任务划分为不同的任务弧段,定义了正常任务弧段和空闲任务弧段,基于Markov过程,提出了正常任务弧段和空闲任务弧段的可靠性建模方法.提出了在考虑测控通信资源的开机准备时间和停机时间的情况下的可靠性建模方法,对测控通信任务重新划分任务弧段,各任务弧段依据相应方法单独建立模型,依次求解各模型可得整个任务的可靠性.最后以具体的测控通信任务为例,计算并分析了不考虑资源开机准备时间和停机时间、仅考虑资源开机准备时间、仅考虑资源停机时间以及考虑资源开机准备时间和停机时间的任务可靠性,验证了模型的正确性.      

燃油调节器壳体裂纹失效分析

针对某型燃油调节器壳体裂纹故障提出一种分析定位方法。通过实物质量分析、光谱分析和ANSYS结构强度仿真,排除材料缺陷和设计缺陷,将故障问题逐渐收敛定位于外部载荷环境的影响,结合断口和疲劳试验载荷谱利用裂纹疲劳弧线反推分析萌生裂纹机理,采用列表梯形法计算出壳体裂纹萌生寿命,将故障分析定位在高周疲劳试验区间。根据高周疲劳寿命试验数据进行频谱扫描,分析高周疲劳试车中发动机转速频率特性、燃油调节器固有频率,并统计试车累计工作时间。结果表明:外部激励频率与燃油调节器的固有频率存在频率上的重合导致了共振失效。为燃油调节器裂纹故障的分析定位提供了思路和方法,可为航空发动机控制体系的发展与研究提供技术支持。     

Possible Role of ion Demagnetization in the Plasma Sheet in Auroral arc and Substorm Generation

Ion demagnetization in the plasma sheet causes the formation of field-aligned current that can trigger a magnetosphere-ionosphere coupling feedback instability, which may play an important role in substorm and auroral arc generation. Since field-aligned currents close ionospheric currents, their magnitude is controlled by ionospheric conductivity. The cause of instability is the impact of increasing upward field-aligned currents on ionospheric conductivity, which in turn stimulates an increase in the field-aligned currents. When the magnitude of these currents becomes sufficiently large for the acceleration of precipitating electrons, a feedback mechanism becomes possible. Upward field-aligned currents increase the ionospheric conductivity that stimulates an explosion-like increase in field-aligned currents. It is believed that this instability may be related to substorm generation. Demagnetization of hot ions in the plasma sheet leads to the motion of magnetospheric electrons through a spatial gradient of ion population. Field-aligned currents, because of their effect on particle acceleration and the magnitude of ionospheric conductivity, can also lead to another type of instability associated with the breaking of the earthward convection flow into convection streams. The growth rate of this instability is maximum for structures with sizes less than the ion Larmor radius in the equatorial plane. This may lead to the formation of auroral arcs with widths of the order of 10 km. This instability is able to explain many features of auroral arcs, including their conjugacy in opposite hemispheres. However, it cannot explain very narrow (less than 1 km) arcs.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.     

基于神经网络预测模型的歼击机结构故障检测方法

提出了一种基于预测神经网络的歼击机结构故障检测新方法 ,与传统的基于模型的非线性系统的故障检测方法相比 ,神经网络方法有着非线性逼近能力强和故障检测实时性好等优点。给出了基于预测神经网络的故障检测方案 ,以及多步直接预测算法和阈值选取原则 ,最后以某型歼击机为例进行了仿真验证 ,仿真结果表明本方法能有效地检测出歼击机的各种结构故障。     

Integrated active fault-tolerant control using IMM approach

An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained     

电分相电弧对全向信标电磁辐射特性的分析

为了分析电气化铁路高速列车电分相电弧对机场全向信标（VOR）的影响,研究其电磁发射特性。利用电磁干扰接收机与频谱仪,分别以点频和扫频方式,对电气化铁路电分相点和普通点进行了大量辐射测试。分析测试数据的异方差性,改进回归分析方法,拟合出电弧在全向信标频段的幅频特性曲线,分析弓网电弧电磁辐射对机场全向信标的影响。结果表明：电分相电磁辐射是随机的,在电分相处比普通点大;现代电气化铁路高速列车经过电分相的测试数据异方差性比较明显,普通的最小二乘估计方法不适用;当电气化铁路电分相点与飞机距离小于4.944 km时,可能会对VOR信号产生影响。     

基于HHT的航空直流串行电弧特征提取方法

由于飞机内部布线空间有限、电弧故障存在发生时间地点随机以及特征不明显等问题，导致检测困难。本文基于航空270 V高压直流（HVDC）系统开展直流串行电弧故障特征提取方法研究，采用希尔伯特黄变换（HHT）提取电弧电流交流分量的时域和频域特征量。选择HHT的固有模态函数IMF5瞬时幅值的峰峰值和标准差作为识别电弧故障的时域特征，与原始信号中提取的时域特征量对比，正常和电弧特征量的区分度更大；选择HHT的固有模态函数IMF1+IMF2、一定频带范围内的瞬时幅值计算得到的谐波功率和作为区分正常和电弧情况的频域特征量。与常用的快速傅里叶变换（FFT）方法相比，HHT三维时频谱能够反映信号的局部特征，HHT方法计算得到的正常和电弧特征量之间的区分度更大，电弧和正常特征量的比值最高可达346。基于HHT的电弧故障特征提取方法能够更好地区分正常和电弧情况，有助于提高电弧故障的检测率，降低虚警率，具有重要的工程应用价值。     

Stable mid-latitude red arcs: Observations and theory

Summary The observational features of the arc are fairly well established. At present, the thermal conduction model appears to explain the red arc features most consistently, but it must be noted that a soft electron flux would give very similar results. Ion temperature measurements in the vicinity of an arc, which should be forthcoming in the very near future, can establish conclusively whether transverse electric fields play any important role in the formation of the arcs. Accepting the assumption that the arcs are the result of energy flowing down from the plasmasphere, the major remaining question is: where does the energy come from and how does it get into the plasmasphere? The various proposed mechanisms discussed in the previous chapter appear feasible, but much work needs to be done before this problem is completely resolved.On leave from the Department of Electrical Engineering, The University of Michigan, Ann Arbor.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

基于滑窗检测的机载设备故障诊断仿真系统

根据机载设备故障诊断的需求,结合飞机性能监控系统的结构框架和机载设备的特点,提出基于快速存取记录器（QAR）记录的飞行数据和滑窗检测原理的机载设备故障诊断算法,并实现相应的机栽设备故障诊断仿真系统。在简述QAR数据译码与数据选择方法的基础上,论述了滑窗检测的原理并对其性能进行分析：设计实现了基于QAR的机载设备故障诊断仿真系统并给出仿真结果。结果表明,该系统可以有效诊断存在的故障,并能大大降低虚警概率,对保障飞行安全具有重要意义。     

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.     

存在外部干扰的网络控制系统故障检测和优化

针对存在时延和丢包的网络控制系统,研究了系统的故障检测滤波器设计和优化设计问题.通过泰勒展开将未知时延处理成外界干扰,采用伯努利随机过程来描述丢包现象.设计观测器产生残差信号,将故障检测问题转化为H∞滤波问题.通过Lyapunov函数方法分析了系统的稳定性.以线性矩阵不等式的形式给出了滤波器存在的充分条件和求解方法.为进一步提高系统对故障信号的灵敏度,采用了一种优化设计方法对故障检测系统进行了优化.最后通过仿真验证了算法的有效性.     

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.     

Cooperative fault detection and recovery in the GNSS positioning of mobile agent swarms based on relative distance measurements

Relative measurements are exploited to cooperatively detect and recover faults in the positioning of Mobile Agent(MA) Swarms(MASs). First, a network vertex fault detection method based on edge testing is proposed. For each edge, a property that has a functional relationship with the properties of its two vertices is measured and tested. Based on the edge testing results of the network, the maximum likelihood principle is used to identify the vertex fault sources. Second, an edge distance testing...     

A demonstration of sub meter GPS orbit determination and highprecision user positioning

High-accuracy orbits have been determined for satellites of the Global Positioning System (GPS), with submeter orbit accuracy demonstrated for two well-tracked satellites. Baselines of up to 2000 km in North America determined with the GPS orbits shows daily repeatability of 0.3-2 parts in 10⁸ and agree with very long baseline interferometry (VLBI) solutions at the level of 1.5 parts in 10 ⁸. Tests used to assess orbit accuracy include orbit repeatability from independent data sets, orbit prediction, ground baseline determination, and formal errors. One satellite tracked for eight hours each day shows RMS errors below 1 m even when predicted more than three days outside of a 1-week data arc. These results demonstrate the powerful relative positioning capability available from differential GPS tracking. Baselines have also been estimated between Florida and sites in the Caribbean region over 1000 km away, with daily repeatability of 1-4 parts in 10⁸. The best orbit estimation strategies included data arcs of 1-2 weeks, process noise models for tropospheric fluctuations, combined processing of GPS carrier phase and pseudorange data, and estimation of GPS solar pressure coefficients     

基于数据关联的故障快速检测

 多数情况下,快速实时地进行故障检测是很重要的。将故障看做是通过多传感器观测的动态模型,进行多传感器多模型概率数据关联,以各个模型的关联结果和设定的阈值为依据,可以有效地实现故障检测。联合概率数据关联（JPDA）算法是解决多传感器多目标跟踪的一个有效方法,文中通过分析概率数据关联算法,对联合概率数据关联算法进行了改进：（1）通过正确地选择阈值,移除小概率事件,进而建立一个近似的确认矩阵;（2）根据被跟踪目标故障跟踪门的相交情况,将跟踪空间进行数学划分,形成若干相互独立的区域;（3）对同一区域内公共有效量测的概率密度值进行衰减,计算出关联概率。仿真对比表明,本文的改进算法能显著减少计算时间,有效提高故障检测的快速性和实时性。     

基于CCFHTLS算法的异步电动机转子故障检测

定子电流经低通滤波器处理后产生高斯有色噪声,限制了异步电机转子故障检测算法的辨识精度。针对该问题,提出一种抑制高斯有色噪声的异步电机故障检测技术。首先对采集到的定子电流进行预处理,利用逆同步旋转变换剔除基波,避免了直接检测时基波对故障辨识精度的影响。然后利用互相关函数(CCF)处理技术对高斯有色噪声的抑制作用,提出基于CCFHTLS算法的电机转子故障诊断技术。针对异步电机转子断条和偏心故障的识别进行试验,结果表明CCFHTLS算法可以有效抑制高斯有色噪声,并保留故障有用信息,显著地提高了故障检测的分辨率。     

基于滑动时间窗主成分分析的液体火箭发动机传感器故障诊断方法

安装在发动机上的各种传感器是发动机状态监测的主要依据,由于工作环境恶劣,传感器失效时有发生。由于发动机运行过程中的性能蜕变和台次差异,现有基于主成分分析（PCA）的传感器故障隔离方法应用条件苛刻且诊断效果有限。针对这些问题,在对发动机数据分析的基础上,将滑动时间窗方法与PCA方法结合,提出双滑动时间窗的PCA方法用于故障传感器的隔离,并基于发动机试车数据进行了方法验证。结果表明：该方法能降低发动机性能蜕变和台次差异对发动机传感器故障诊断的影响,没有参数相关性的限制,可以实现对四种常见传感器故障的有效隔离,以及对两种发动机试验过程中故障的准确检测。研究证明了高速运转系统性能蜕变和强耦合复杂大系统台次差异对基于数据的故障诊断方法效果的影响,验证了在线学习/训练算法对这两种现象的鲁棒性。     

液体火箭发动机故障检测与诊断的反向选择算法

基于生物免疫系统中的反向选择原理,提出了液体火箭发动机地面试车故障检测与诊断的新方法,对检测器的产生、检测器数量的确定以及故障诊断的实现等几个问题进行了分析。基于发动机地面试车数据的验证结果表明,该方法能有效提高算法的训练效率、实时性能和诊断结果的准确性。