Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

Abnormal changes of dynamic derivatives at low reduced frequencies

A number of studies have found that abnormal changes of dynamic derivatives occurred at very low reduced frequencies, but its inducement mechanism is not very clear. This paper has researched the abnormal changes and analyzed the influence on some parameters by solving the unsteady flow around forced oscillation airfoils based on Navier-Stokes equations. Results indicate that when the reduced frequency approaches to zero, the dynamic derivatives obtained by the numerical method will diverge. We have also proven it in theory that this phenomenon is not physical but completely caused by numerical singularity. Furthermore, the abnormal phenomenon can be effectively mitigated by using the time spectral method to solve the aerodynamic forces and the integral method to obtain the dynamic derivatives. When the reduced frequency is in the range of 0.001–0.01, the dynamic derivative maintains nearly unchanged for the whole speed region. This study can provide a reference for the reasonable choice of the reduced frequency in calculations and experiments of dynamic derivatives.     

双框架飞机蒙皮检测机器人切换运动控制方法

针对一种双框架飞机蒙皮检测机器人,通过分析该机器人在飞机表面上的受力情况,在飞机表面该机器人受到了非完整约束,基于牛顿-欧拉法建立了机器人非完整约束动力学模型。根据机器人机械结构和运动步态分析,将该非完整机器人系统分为子系统A和子系统B。为了实现机器人在飞机表面运动,采用反演技术和快速Terminal滑模控制相结合的思想对系统设计了控制器,提出了一种反演-滑模控制方法;对于非完整机器人子系统A和子系统B,设计了一种基于事件驱动的切换策略,实现了机器人对期望轨迹的全局渐近跟踪,并利用Lyapunov稳定性证明系统的跟踪误差收敛。仿真和试验表明,采用该切换策略和反演-滑模控制方法,双框架飞机蒙皮检测机器人可以在飞机表面自由运动并进行损伤检测,具有良好的可靠性和稳定性。     

Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

Gas Turbine Engines (GTEs) are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a) diverse ambient conditions and control settings, (b) every possible combination of degradation symptoms, and (c) a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.     

硅半球深腔结构无损检测技术

半球深腔的加工质量是决定硅基MEMS半球陀螺精度的关键因素之一，及时检测半球深腔的形貌参数并将其反馈至加工过程，是确保高性能MEMS半球陀螺研制成功的重要措施。由于硅半球深腔的深度较大，台阶仪和光学显微成像系统无法对半球深腔的形貌特征进行有效测量。因此,需要将硅深腔结构剖开后采用扫描电镜(SEM)进行检测。这种检测方式时间周期长，且属于破坏性的样本检测，效率和测试精度都较低。提出以硅半球深腔为模具，利用PDMS铸模将硅半球深腔的结构尺寸和表面形貌转移到PDMS凸起的半球模型上，通过检测PDMS半球模型的尺寸结构和表面形貌，即可反推出硅半球深腔的尺寸特征。经实验验证，脱模后的PDMS模型可以准确地反映出半球深腔的尺寸信息，测量结果的不确定度小于5‰，有效解决了硅半球深腔无损检测的难题。     

Aircraft nonlinear stability analysis and multidimensional stability region estimation under icing conditions

Icing is one of the crucial factors that could pose great threat to flight safety,and thus research on stability and stability region of aircraft safety under icing conditions is significant for control and flight.Nonlinear dynamical equations and models of aerodynamic coefficients of an air craft are set up in this paper to study the stability and stability region of the aircraft under an icing condition.Firstly,the equilibrium points of the iced aircraft system are calculated and analyzed based on the theory of differential equation stability.Secondly,according to the correlation theory about equilibrium points and the stability region,this paper estimates the multidimensional stability region of the aircraft,based on which the stability regions before and after icing are compared.Finally,the results are confirmed by the time history analysis.The results can give a reference for stability analysis and envelope protection of the nonlinear system of an iced aircraft.     

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.     

一种EXCEL-VB环境下的动态生产作业信息平台

基于EXCEL软件函数和算法,借助VB程序语言,建立了一种信息动态化的生产作业系统.通过核心参数,实现对生产计划、生产进度、物料库存、调度控制、工时考核、生产指令发放、超缺货告警等关键生产参数的实时监控.平台使用后,在生产现场计划和调度管理的各项关键指标中,计划编制时间缩短了80％,零件库存降低39.3％,计划完成率提高31.2％,生产均衡由原来的经常缺件提高到现在的连续8个月生产无停顿,加班减少70％以上,且年度产品准时交付率达到了99.04％.     

基于气压高度辅助的机载自主完好性监测算法

卫星接收机自主完好性监测是指根据用户接收机的多余观测值监测用户定位结果的完好性,其目的是在导航过程中检测出发生故障的卫星,并保障导航定位精度。针对卫星接收机自主完好性监测算法可用性不足的问题,结合机载实际导航系统配置,提出了一种基于气压高度表辅助的机载自主完好性监测算法。综合利用卫星导航系统及气压高度表观测信息,建立联合系统的观测模型,推导了基于多解分离的完好性监测及保护级别计算方法。仿真结果表明,相比于传统的接收机自主完好性监测算法,该算法在可见星为5颗时仍能识别故障卫星。该算法具有更好的故障检测能力及可用性,能有效提高卫星导航系统的完好性监测性能,从而保证卫星导航系统的精度和可靠性。