A cost driven predictive maintenance policy for structural airframe maintenance

Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring (SHM) systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,lead ing to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter (EKF) with a first order perturbation (FOP) method.By using the information given by this prognostics method,a novel cost driven predictive maintenance (CDPM) policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is imple mented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies:scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.     

数字全息测量超高速撞击碎片云实验研究

在中国空气动力研究与发展中心(CARDC)超高速碰撞中心(HIRC)7.6 mm超高速碰撞设备的基础上，搭建纳秒级脉冲激光数字全息系统。提出滤波片和衰减片组合布置，减弱超高速碰撞等离子体自发光、提高信噪比的方法。实验获得了2.25 mm铝球弹丸以4.0 km/s的速度撞击0.5 mm厚铝板形成碎片云的全息图。采用小波变换算法对碎片云全息图进行重建，得到超高速撞击碎片云的三维结构和碎片大小。碎片云的轮廓呈椭球型，分为碎片云的前端、核心和外壳，碎片主要分布在弹丸破碎形成的碎片云核心，存在大碎片，且分布较集中，对后板的损伤也严重     

Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles

This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for realtime applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker's performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-ofthe-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker's robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016.     

Particle swarm optimization applied to hypersonic reentry trajectories

This paper presents the novel use of the particle swarm optimization(PSO)to generate the end-to-end trajectory for hypersonic reentry vehicles in a quite simple formulation.The velocitydependent bank angle profile is developed to reduce the search space of unknown parameters based on the constrained PSO algorithm.The path constraints are enforced by setting the fitness function to be infinite on condition that the particles violate the maximum allowable values.The PSO algorithm also provides a much easier means to satisfy the terminal conditions by adding penalty terms to the fitness function.Furthermore,the approximate reentry landing footprint is fast constructed by incorporating an interpolation model into the standardized bank angle profiles.Numerical simulations demonstrate that the PSO method is a feasible and flexible tool to generate the end-to-end trajectory and landing footprint for hypersonic reentry vehicles.     

基于Kalman滤波的变体飞行器T-S模糊控制

针对变体飞行器的跟踪控制问题,提出了一种基于Kalman滤波的T-S模糊控制方法。考虑飞行器系统状态不可测,引入惯导数据作为辅助信息,利用Kalman滤波算法融合飞控信息与惯导信息实现状态估计。由于变体飞行器在不同变形结构下气动特性变化较大,为便于控制器设计,采用小扰动线性化方法得到飞行器在不同平衡点处的局部线性模型,并通过状态反馈方法设计局部控制器,局部线性模型和局部控制器通过模糊集和模糊规则聚合成一个连续光滑的全局T-S模糊模型和T-S模糊控制器。通过综合Kalman滤波器与T-S模糊控制器得到一个基于Kalman滤波的T-S模糊控制器。仿真结果表明,该控制器在变形过程中能够实现状态估计,保证飞机的跟踪性能。     

Development,integrated investigation,laboratory and in-flight testing of Chibis-M microsatellite ADCS

Design, analytical investigation, laboratory and in-flight testing of the attitude determination and control system (ADCS) of a microsatellites are considered. The system consists of three pairs of reaction wheels, three magnetorquers, a set of Sun sensors, a three-axis magnetometer and a control unit. The ADCS is designed for a small 10–50 kg LEO satellite. System development is accomplished in several steps: satellite dynamics preliminary study using asymptotical and numerical techniques, hardware and software design, laboratory testing of each actuator and sensor and the whole ADCS. Laboratory verification is carried out on the specially designed test-bench.In-flight ADCS exploitation results onboard the Russian microsatellite “Chibis-M” are presented. The satellite was developed, designed and manufactured by the Institute of Space Research of RAS. “Chibis-M” was launched by the “Progress-13M” cargo vehicle on January 25, 2012 after undocking from the International Space Station (ISS). This paper assess both the satellite and the ADCS mock-up dynamics. Analytical, numerical and laboratory study results are in good correspondence with in-flight data.     

Particle simulation of grid system for krypton ion thrusters

The transport processes of plasmas in grid systems of krypton (Kr) ion thrusters at different acceleration voltages were simulated with a 3D-PIC model, and the result was compared with xenon (Xe) ion thrusters. The variation of the screen grid transparency, the accelerator grid current ratio and the divergence loss were explored. It is found that the screen grid transparency increases with the acceleration voltage and decreases with the beam current, while the accelerator grid current ratio and divergence loss decrease first and then increase with the beam current. This result is the same with Xe ion thrusters. Simulation results also show that Kr ion thrusters have more advantages than Xe ion thrusters, such as higher screen grid transparency, smaller accelerator grid current ratio, larger cut-off current threshold, and better divergence loss characteristic. These advantages mean that Kr ion thrusters have the ability of operating in a wide range of current. Through comprehensive analyses, it can be concluded that using Kr as propellant is very suitable for a multi-mode ion thruster design.     

运动单站干涉仪相位差直接定位方法

针对传统干涉仪测向定位在低信噪比(SNR)下性能降低、多频段系统复杂等缺点,本文直接利用运动单站干涉仪模糊相位差(PD)实现对地面静止辐射源定位.为解决该定位问题,结合干涉仪相位差的几何特性,提出了一种多角度距离参数化高斯和滤波(MB-RP-GMF)定位解算方法,利用相位差误差检测获得多个初始方位角,通过距离参数化得到一组滤波初始值,采用扩展卡尔曼滤波(EKF)进行定位计算.仿真结果表明,该定位方法在低信噪比下优于仅测向(BO)定位,且仅采用一组干涉仪便可对多个频段辐射源进行定位.     

Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree

Multirotor has been applied to many military and civilian mission scenarios. From the perspective of reliability, it is difficult to ensure that multirotors do not generate hardware and software failures or performance anomalies during the flight process. These failures and anomalies may result in mission interruptions, crashes, and even threats to the lives and property of human beings. Thus, the study of flight reliability problems of multirotors is conductive to the development of the drone industry and has theoretical significance and engineering value. This paper proposes a reliable flight performance assessment method of multirotors based on an Interacting Multiple Model Particle Filter (IMMPF) algorithm and health degree as the performance indicator. First, the multirotor is modeled by the Stochastic Hybrid System (SHS) model, and the problem of reliable flight performance assessment is formulated. In order to solve the problem, the IMMPF algorithm is presented to estimate the real-time probability distribution of hybrid state of the established SHS-based multirotor model, since it can decrease estimation errors compared with the standard interacting multiple model algorithm based on extended Kalman filter. Then, the reliable flight performance is assessed with health degree based on the estimation result. Finally, a case study of a multirotor suffering from sensor anomalies is presented to validate the effectiveness of the proposed method.