支撑产品线统标统型的研发模式创新与实践研究

针对航空电子研发企业产品型谱不收敛、新技术应用风险高等问题和挑战，提出了一种用于支撑产品线统标统型的新型研发模式，通过构建支撑产品线统标统型的产品技术研发体系，推进平台CBB在产品线各层级中的广泛共享复用，实现产品的通用化、系列化、组合化研制。实践结果表明，该研发模式可有效促进企业研发成本、质量、进度的有机统一。     

卫星导航有源接收天线噪声温度测量

卫星导航有源接收天线的噪声温度是导航接收系统的关键技术指标之一。针对卫星导航有源天线总体噪声温度无法测量的问题，研制了两台口面型噪声源，口面噪声源主要由辐射体、辐射体物理温度控制和温度测量仪等组成。两个口面噪声源在L和S波段分别提供高低温标准噪声温度，采用Y系数测量方法测量有源接收天线的总体天线噪声温度。测量了某卫星导航有源天线的总噪声温度，在(1.19～1.29) GHz的频率范围内，中心频率1.24 GHz上噪声温度测量结果为206 K，但是在1.266 GHz频率点上噪声温度测量大于4 000 K，说明天线与滤波器之间、滤波器与放大器之间存在设计问题或其它问题，体现出测量有源天线噪声温度的必要性。     

基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法

组合导航能够将多种类型的导航信息进行结合,实现优势互补,因此成为了目前导航应用领域的主要发展方向。然而,导航信息的增多势必会引入更多的风险源,从而降低导航系统的可靠性。基于联邦滤波的容错方法是目前抑制故障信息影响的主要解决手段,但是现有的故障容错方法普遍采用统一的检测机制,没有根据各个导航子系统的误差传播特性针对性地构建故障检测模型,因此会引起较高的误警率与漏检率。针对上述问题,提出了基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法。通过构建面向INS/BDS/地磁不同导航信息的故障检测函数,能够实现更加准确的矢量化信息分配,从而可以有效避免可用导航信息的损失以及故障导航信息对整体系统的影响。仿真结果表明,提出的方法可以有效隔离不同类型的故障信息,并减小其对无故障导航信息及整体系统的影响,从而提高了组合导航的精度和可靠性。     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.     

A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment. Belief Rule Base (BRB) is an interpretable expert system gradually applied in fault diagnosis. However, the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features. In addition, the interpretability of BRB-based fault diagnosis is destroyed in the optimization process, which reflects in two aspects: deviation from the initial expert judgment and over-optimization of parameters. To solve these problems, a new interpretable fault diagnosis model based on BRB and probability table, called the BRB-P, is proposed in this paper. Compared with the traditional BRB, the BRB-P constructed by the probability table is more accurate. Then, the interpretability constraints, i.e., the credibility of expert knowledge, the penalty factor and the rule-activation factor, are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P. A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.     

Synchronous chirp mode extraction: A promising tool for fault diagnosis of rolling element bearings under varying speed conditions

As critical components in modern aerospace productions, rolling element bearings(REBs) generally work under varying speed conditions, which brings great challenges to their operating health monitoring. Some novel time–frequency decomposition(TFD) algorithms are established recently to extract nonlinear features from the non-stationary signals effectively, which are promising for realizing fault diagnosis of REBs under varying speed conditions. However, numerous personal experiences must be incor...     

基于解耦矢量控制的五相永磁同步电机容错控制策略

针对五相永磁同步电机两相开路故障的运行工况,提出了一种基于解耦矢量控制理论的故障容错控制策略.该策略通过在两相开路故障下的同步旋转坐标系内建立电机的数学模型,构造解耦变换矩阵,将电流控制中id和iq轴解耦,达到两相开路故障下容错运行的目的 .仿真结果表明,在五相永磁同步电机两相开路故障下,基于解耦矢量控制理论的故障容错控制策略能够实现电机驱动系统的高品质运行,具有控制简单、转矩波动小、容错能力强等优点.     

一种基于模型预测的火星返回推力器容错控制再分配方法

针对火星返回上升器由于环境等因素造成的推力器故障导致的姿态控制系统失稳而难以安全返回的严重问题，提出基于模型预测的动态容错控制再分配方法。根据推力器动态特性建立上升器推力分配模型，对模型参数误差进行实时估计从而修正分配模型，根据模型预测自适应推力再分配方法实施容错控制。同时，将推力器输出限制作为优化求解器的约束，并将推力器故障模型作为优化求解的约束域，实现最小化分配误差和最小化燃料消耗意义下的最优推力再分配。计算机仿真表明了该方法的可行性和实用性，获得了满意的结果，它能使推力器输出推力误差降低60%以上，姿态控制系统能在故障状态下3～5 s快速镇定。     

Design,modeling, and control of morphing aircraft:A review

A morphing aircraft can adapt its configuration to suit different types of tasks, which is also an important requirement of Unmanned Aerial Vehicles(UAV). The successful development of an unmanned morphing aircraft involves three steps that determine its ability and intelligent: configuration design, dynamic modeling and flight control. This study conducts a comprehensive survey of morphing aircraft. First, the methods to design the configuration of a morphing aircraft are presented and analyzed...     

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...