A new modeling scheme for powered parafoil unmanned aerial vehicle platforms: Theory and experiments

A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle (PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.     

基于层次分析法的综合射频架构选取技术研究

综合射频系统是作战飞机航空电子系统的重要组成部分，它们是作战飞机通过无线电方式感知外界信息的重要系统。系统架构的选取直接影响了飞机的作战性能，针对以往架构选取依赖大量定性分析而缺乏定量对比问题，提出了基于层次分析法的综合射频架构选取技术，该技术能够有效地根据不同的任务需求和平台需求，综合考虑定量和定性因素，给出更为合理对比分析结果，为综合射频系统的系统设计提供技术支撑。该技术也可推广到航空领域其他多目标决策问题的应用。     

三维攻击角度约束部分制导控制一体化设计

针对侧滑转弯(STT)导弹带有攻击角度约束的机动目标拦截问题，提出一种基于自适应终端滑模动态面控制的三维部分制导控制一体化(PIGC)设计方法。首先，建立了针对机动目标拦截的侧滑转弯导弹三维部分制导控制一体化设计模型，且不需要导弹速度微分体轴系分量信息。然后，使用终端滑模控制理论构建误差向量与虚拟控制量，达成精确拦截与攻击角度约束的控制目的；引入有限时间非线性收敛扩张状态观测器(ESO)来在线估计系统不确定性；设计自适应算子与自适应更新律对观测器的估计误差进行补偿，以提高方法的鲁棒性。最后，三维空间拦截仿真校验了方法在提高拦截精度与增强角度约束收敛性能的有效性。     

纯电动城市客车EMB执行机构驱动电机制动性能分析

传统气压制动系统存在压缩机、制动油箱和空气管路,占据较大空间,而采用电子机械制动(EMB)系统代替,不但可以减小空间,还能实现对夹紧力的快速响应和精确控制。以纯电动城市客车为目标车型,讨论了EMB执行机构方案,根据传统气压盘式制动器最大夹紧力推导出电机的堵转转矩和空载转速。据此设计了永磁无刷直流驱动电机,堵转转矩为10 N·m,空载转速为370 r/min。在Maxwell 2D中搭建驱动电机有限元模型,分析电机在消除间隙阶段和夹紧力增加阶段的制动性能。结果表明所设计的驱动电机制动性能可满足要求。     

热障涂层表面裂纹水浸超声检测

为有效检测热障涂层(TBCs)表面裂纹,进行了热障涂层表面裂纹水浸超声检测技术的应用研究。以常用的大气等离子喷涂(APS)热障涂层作为试验材料,采用水浸超声检测回波声程差和C扫描图像对不同深度、宽度和长度的热障涂层表面裂纹进行了表征。结果表明:热障涂层基体材料对热障涂层表面裂纹的检测效果无明显影响;通过优化检测参数和合理选用仪器探头,利用水浸超声方法可实现对热障涂层表面深度为0.1 mm、长度为2 mm裂纹的有效检测;水浸超声检测技术可以获得裂纹的长度、深度参数,是解决热障涂层表面裂纹检测问题的有效方法。      

Evaluation and improvement of coastal GNSS reflectometry sea level variations from existing GNSS stations in Taiwan

Global sea level rise due to an increasingly warmer climate has begun to induce hazards, adversely affecting the lives and properties of people residing in low-lying coastal regions and islands. Therefore, it is important to monitor and understand variations in coastal sea level covering offshore regions. Signal-to-noise ratio (SNR) data of Global Navigation Satellite System (GNSS) have been successfully used to robustly derive sea level heights (SLHs). In Taiwan, there are a number of continuously operating GNSS stations, not originally installed for sea level monitoring. They were established in harbors or near coastal regions for monitoring land motion. This study utilizes existing SNR data from three GNSS stations (Kaohsiung, Suao, and TaiCOAST) in Taiwan to compute SLHs with two methods, namely, Lomb–Scargle Periodogram (LSP)-only, and LSP aided with tidal harmonic analysis developed in this study. The results of both methods are compared with co-located or nearby tide gauge records. Due to the poor quality of SNR data, the worst accuracy of SLHs derived from traditional LSP-only method exceeds 1?m at the TaiCOAST station. With our procedure, the standard deviations (STDs) of difference between GNSS-derived SLHs and tide gauge records in Kaohsiung and Suao stations decreased to 10?cm and the results show excellent agreement with tide gauge derived relative sea level records, with STD of differences of 7?cm and correlation coefficient of 0.96. In addition, the absolute GNSS-R sea level trend in Kaohsiung during 2006–2011 agrees well with that derived from satellite altimetry. We conclude that the coastal GNSS stations in Taiwan have the potential of monitoring absolute coastal sea level change accurately when our proposed methodology is used.     

Hypervelocity impact induced shock acoustic emission waves for quantitative damage evaluation using in situ miniaturized piezoelectric sensor network

Manmade debris and natural meteoroids, travelling in the Low Earth Orbit at a speed of several kilometers per second, pose a severe safety concern to the spacecraft in service through the HyperVelocity Impact(HVI). To address this issue, an investigation of shock Acoustic Emission(AE) waves induced by HVI to a downscaled two-layer Whipple shielding structure is performed,to realize a quantitative damage evaluation. Firstly a hybrid numerical model integrating smoothparticle hydrodynamics and finite element is built to obtain the wave response. The projectiles, with various impact velocities and directions, are modelled to impact the shielding structure with different thicknesses. Then experimental validation is carried out with built-in miniaturized piezoelectric sensors to in situ sense the HVI-induced AE waves. A quantitative agreement is obtained between numerical and experimental results, demonstrating the correctness of the hybrid model and facilitating the explanation of obtained AE signals in experiment. Based on the understanding of HVI-induced wave components, assessment of the damage severity, i.e., whether the outer shielding layer is perforated or not, is performed using the energy ratio between the regions of ‘‘high frequency" and ‘‘low frequency" in the acquired AE signals. Lastly, the direct-arrival fundamentalsymmetric wave mode is isolated from each sensing signal to be input into an enhanced delay-andsum algorithm, which visualizes HVI spots accurately and instantaneously with different sensor network configuration. All these works demonstrate the potential of quantitative, in situ, and real time HVI monitoring using miniaturized piezoelectric sensor network.     

Real-time clock comparison and monitoring with multi-GNSS precise point positioning: GPS,GLONASS and Galileo

The precise point positioning (PPP) technique is widely used in time and frequency applications. Because of the real-time service (RTS) project of the International GNSS Service, we can use the PPP technique for real-time clock comparison and monitoring. As a participant in the RTS, the Centre National d’Etudes Spatiales (CNES) implements the PPPWIZARD (Precise Point Positioning with Integer and Zero-difference Ambiguity Resolution Demonstrator) project to validate carrier phase ambiguity resolution. Unlike the Integer-PPP (IPPP) of the CNES, fixing ambiguities in the post-processing mode, the PPPWIZARD operates in the real-time mode, which is also called real-time IPPP (RT-IPPP). This paper focuses on applying the RT-IPPP for real-time clock comparison and monitoring. We review the principle of real-time clock comparison and monitoring, and introduce the methodology of the RT-IPPP technique. The observations of GPS, GLONASS and Galileo were processed for the experiments. Five processing modes were provided in the experiment to analyze the benefits of ambiguity resolution and multi-GNSS. In the clock comparison experiment, the average reduction ratios of standard deviations with respect to the G PPP mode range from 9.7% to 35.0%. In the clock monitoring experiment, G PPP mode can detect clock jumps whose magnitudes are larger than 0.9 ns. The RT-IPPP technique with GRE PPP AR (G) mode allows for the detection of any clock jumps larger than 0.6 ns. For frequency monitoring, G PPP mode allows detection of frequency changes larger than 1.1 × 10⁻¹⁴. When the RT-IPPP technique is applied, monitoring with GRE PPP AR (G) mode can detect frequency changes larger than 6.1 × 10⁻¹⁵.     

基于北斗三号的长基线共视时间比对

卫星导航共视时间比对一直是远距离时间比对的重要方法之一。使用我国最新发射的北斗三号全球导航卫星，基于中科院国家授时中心（NTSC）和捷克光电研究院（TP）各自的时间产生和保持系统，开展了中捷北斗三号长基线共视时间比对试验。本文对中捷两站各自的北斗卫星可视数及其卫星高度角情况进行了统计分析，利用Vondark滤波对时间比对结果进行了降噪处理，最后将北斗三号共视时间比对结果与北斗二号及GPS共视时间比对结果进行了比较。结果表明：北斗三号在当前全球组网阶段中捷共视可视卫星数比北斗二号还少的情况下，其共视时间比对精度达到1.16ns，较北斗二号提升约19%，与GPS更为接近。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.