XPNAV-1卫星实测数据处理与分析

为展示我国首颗脉冲星导航试验卫星XPNAV-1载荷实测数据的性能,采用与国外公布实测数据对比分析的方法,从观测数据计数统计、光子能量响应、计时性能三方面介绍了XPNAV-1卫星发布数据的性能。分析结果表明,我国自主研发的探测器观测结果与国外天文卫星载荷观测结果的一致性较好,未来进一步深入发展将有望应用于脉冲星导航。     

航天器传动部件振动数据稀疏多分类智能故障诊断

针对航天器传动链机械零部件发生故障时训练数据稀缺以及信号的强非平稳\非线性特点,提出了动态数据驱动的子空间稀疏多分类智能故障诊断算法。首先对采集的单一传感器动态信号进行分形小波变换,生成常规和非常规二进小波尺度。然后对生成的各子空间提出基于故障能量指标的稀疏多分类器,以监测部件的故障特征频率为搜索目标,在子空间的包络解调谱上计算特征频率及其倍频附近的能量峰值,导出特定故障模式的稀疏评价指标,以各种故障模式的最大值识别和判定故障类型。所提出的算法完全实现了无人工监督的智能故障诊断。最后以航天器轴承故障诊断为例验证了该算法的有效性。所研究算法的泛化能力较强,技术路线同样适用于其他航天器传动部件的在线监测与故障智能预警。     

基于综合电子的某型运载器电气系统方案设计

为降低全生命周期成本,提高航天运载器综合竞争力,提出了基于综合电子方案的某型运载器电气系统设计实现方法。首先对该运载器功能需求进行了分析,建立了基于综合电子的系统分布式集成逻辑架构,然后分别就系统任务规划与操作系统分区结构、基于时间触发以太网(TTE)的一体化机内外总线体制以及系统容错处理机制等关键技术及实现途径开展了分析论证。本方案提出的相关软、硬件设计方法有利于实现系统快速集成与系统内资源共享,同时为实现冗余容错控制、在线任务规划等功能提供了便利。     

基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断

针对从行星齿轮箱非线性、非平稳振动信号特征提取困难的问题,提出了一种基于自适应复合多尺度排列熵(ACMPE)、改进监督型自组织增量学习神经网络界标点等度规映射(ISSL-Isomap)和灰狼群优化支持向量机(GWO-SVM)相结合的行星齿轮箱故障诊断方法。利用ACMPE从复杂域提取振动信号的故障特征,构建高维故障特征集;采用ISSL-Isomap方法对高维故障特征集进行维数约简,提取出低维、敏感故障特征;应用GWO -SVM分类器对低维故障特征进行模式识别,判断故障类型。行星齿轮箱故障诊断实验结果分析表明:与多尺度排列熵(MPE)、复合多尺度排列熵(CMPE)等特征提取方法相比,ACMPE方法在分类效果和识别精度上更具优势;与局部切空间排列(LTSA)、等度规映射(Isomap)、加权Isomap(W-Isomap)、监督Isomap(S-Isomap)和监督型自组织增量学习神经网络界标点Isomap(SSL-Isomap)等降维方法进行比较,ISSL-Isomap方法降维效果最佳;所提方法的故障识别率达到100%,具有一定优越性。      

基于MED和LMD的自动倾斜器轴承广义Shannon熵谱分析

针对轴承信号微弱故障特征易被强背景噪声淹没的问题,提出采用最小熵反褶积,通过逆滤波器最优化设计,对目标信号进行降噪处理,其峭度值提高了约3.8倍,增强了信号的微弱故障特征;针对非平稳非线性信号频率成分复杂难以解调的问题,提出采用局部均值分解(LMD)和峭度-相关系数筛选准则,其可对非平稳非线性信号进行自适应分解和最优重构,提高了信号的信噪比;针对信号耦合调制及边频突出的问题,通过引入广义Shannon熵进行包络谱带内降噪处理,信号一阶故障特征调制频率与故障特征频率的幅度比降低了24%~43%。通过实验室信号及某型直升机自动倾斜器轴承故障诊断地面试验的分析结果验证了该方法的合理性和可行性。      

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.     

基于机匣信号的滚动轴承故障卷积神经网络诊断方法

针对在滚动轴承故障激励下的机匣微弱故障特征,提出了基于卷积神经网络(CNN)的故障诊断方法。利用矩阵图法、峭度图法以及小波尺度谱法3种振动信号的预处理方法,将一维原始信号转换为图像信号;利用卷积神经网络对故障进行识别。通过比较分析发现:通过连续小波尺度谱更易提取滚动轴承的故障特征,其故障识别率达到95.82%,均高于其他几种振动信号预处理方法;由于卷积神经网络可以利用深层网络结构自适应地提取滚动轴承故障特征,比传统支持向量机(SVM)方法的故障识别率高约7%。结果证明了该方法的有效性与可行性,且具有较好的泛化能力和稳健性。      

基于小波变换和神经网络的直升机旋翼不平衡故障诊断方法

依据直升机旋翼不平衡故障空间与多点机体振动空间存在一对一映射关系的理论,采用某旋翼试验台设置桨距不平衡、质量不平衡以及后缘调整片不平衡的方法获取试验数据。利用小波变换和神经网络处理直升机机体振动信号,并对直升机旋翼单故障和复合故障进行诊断。最终实现了一种利用小波变换处理机体振动信号诊断旋翼不平衡故障的方法。     

ATM performance measurement in Europe,the US and China

Air traffic management (ATM) performance and the metrics used in its assessment are investigated for the first time across the three largest ATM world regions: Europe, the US and China. The market structure and flow management practices of each region are presented. A wide range of performance data across these three regions is synthesised. For topological and performance assessment, the notion of a 'sufficient' sample is often non-intuitive: many metrics may behave non-monotonically as a function of sampling fraction. Missing and under-developed metrics are identified, and the need for a balance between standardisation and flexibility is proposed. Longitudinal and cross-sectional metric trade-offs are identified.     

导航辅助下基于LAS码的天基多目标测控

中继卫星的天基测控是解决中低轨道航天器大范围、长时间、多目标测控的有效途径,但用户星到中继卫星的时延误差、多普勒频移校正误差等参数会对多目标测控带来干扰。针对该问题,首先分析了定位误差引起的时延误差、多普勒频移校正误差等因素,设计了天基多目标前向链路遥控SMA(S-band Multiple Access,S频段多址)信号形式和反向遥测链路SMA信号形式,选择与时延校正误差相匹配的LAS(Large Area Synchronous,大区域同步)码作为反向遥测信息的扩频码,建立了导航辅助的终端时延和频率预校正方案模型,可以有效消除多用户干扰。仿真表明,当Eb/N0≥10.5dB时,前向遥控信息误码率pe≤1×10~(-6),反向遥测信息误码率pe≤1×10~(-6),使用LAS码比Gold序列约有2dB性能改善,为基于我国天链卫星的中低轨道卫星稳定运行、载人航天交会对接以及后续空间站建设等任务的测控提供重要参考。