磁层氧离子向磁鞘泄漏的机制和效应

１９９７年 １月 １０日磁暴期间， Ｇｅｏｔａｉｌ卫星在向阳侧的磁鞘中观测到了磁层氧离子突增事件．这些氧离子的出现和磁鞘中存在很强的南向行星际磁场有关．事件期间向阳面发生了准静态的磁重联，氧离子流存在由北向南的速度分量．通量突增过程具有逆向和正向能量色散现象，磁层内部只有氧离子有可能被梯度漂移输送到重联区，所以只有氧离子在磁鞘中持续地被观测到．估计氧离子的逃逸速率为 ０．６１× １０２３／ｓ，大约为环电流氧离子输入率的 ３３％．大量的环电流氧离子由磁层跑到了磁鞘，导致环电流指数 ＡＳＹ－Ｈ呈现明显的非对称性．     

九自由度运动模拟器相对位置精度分析与指标分解

摘要： 九自由度运动模拟器是标定航天器交会对接GNC子系统中交会对接光学成像敏感器（CRDS）的关键设备,具有较高的相对位置精度要求.以此为出发点,对影响系统综合指标的误差组成进行系统性分析.梳理各误差分量的关系及相互作用原理,建立误差模型,通过对系统综合指标进行合理的分解与分配,最终使九自由度运动模拟器系统精度达到设计要求.     

惯导系统两类冲击环境条件之间的等效转换

GJB150A采用冲击响应谱的形式来描述复杂冲击环境条件,有助于提升惯导系统等航天电子设备地面试验的真实性。但冲击响应谱试验结果的较大差异性也给产品研制过程中的试验研究带来很大困扰。为了解决试验真实性与试验结果较大差异性之间的矛盾,提出了一种将复杂冲击条件转换为经典冲击条件的等效方法,用经典冲击波形等效冲击响应谱,给出了等效转换的基本准则,基于数值试验结果导出了等效转换公式。经试验验证,按照该方法转换得到的经典波形冲击试验结果接近多次冲击响应谱冲击试验结果的平均水平,表明该方法有效。     

Inconel718多通道放电烧蚀铣削加工技术研究

为了提高高温合金Inconel718电火花放电烧蚀加工效率,改善工件表面质量,使用多通道放电烧蚀加工新方法对Inconel718进行多通道放电烧蚀铣削加工试验。研究表明:在多通道放电烧蚀加工过程中,放电通道的个数是随机的;在一个脉冲放电周期中,随着通道数目的增加,电压波形呈阶梯下降,总回路电流波形呈阶梯上升。对比常规电火花烧蚀加工,多通道放电烧蚀加工的总回路电流提高,使得材料蚀除率提高73.7%;同时,多通道放电分散放电能量,使得多通道放电烧蚀加工后工件表面粗糙度较常规电火花烧蚀加工下降14.6%,工件表面微裂纹变少,裂纹的宽度和长度变小。     

基于北斗短报文的远程图像通信系统设计

北斗短报文功能在国防、民生和应急救援等领域都具有很强的应用价值。利用北斗短报文通信费用低廉的优点,设计了一款可进行远程图像传输的系统。针对北斗短报文通信的局限性,提出了相应的数据传输机制以保证远程数据传输的可靠性与有效性,并对系统功能进行了验证。测试结果表明,系统能够较快地完成图像数据传输的功能。     

非工作状态下电液伺服机构运动分析

针对电液伺服机构安装到运载火箭后存在非工作状态下承受外力并产生被动运动的现状,分析伺服机构的受力情况并构建内部液体流动回路,研究非工作状态下伺服机构在外力作用时的运动特性。发现伺服阀零位特性以及伺服机构高压回路密封性能是影响伺服机构非工作状态下运动特性的主要因素。在外力作用下发生运动时油缸某一侧往往处于抽真空状态。使用零位特性良好的伺服阀以及高压回路泄漏小的伺服机构,在外力作用下将处于双向不动或单向可动的状况。利用典型产品开展了物理试验,验证了结论的有效性。     

开绕组电机驱动用双逆变器死区效应补偿方法

与传统两电平逆变器的死区效应不同,开绕组电机驱动系统中双逆变器的开关死区将导致零序电压和零序电流,使绕组电流出现低频次谐波,影响系统的性能。提出了一种双逆变器开关死区导致的零序电压的补偿方案,通过扩展占空比最大的一路开关信号的导通时间,来插入一个补偿电压矢量,利用补偿电压矢量产生的零序电压抵消开关死区导致的零序电压,从而抑制系统的零序电压和零序电流。通过在开绕组感应电机驱动系统中的仿真和试验,验证了死区补偿方法的有效性。     

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.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.     

融合静电信号和气路参数的发动机性能评估方法

为了研究静电监测技术对航空发动机的监测效果,开展了航空涡喷发动机静电监测台架实验,获得了140个完整试车阶段的静电信号数据。针对发动机传统气路性能评估方法的信息源有限的问题,提出了一种融合静电信号和气路参数的发动机性能评估流程和方法,进一步采用了基于逻辑回归模型的评估算法对发动机的综合性能进行量化评估,通过融合试车实验的静电数据和性能参数,对发动机的健康状态评价方法进行研究。结果表明:当健康值为0.9以上可以认为发动机为健康状态,当健康值小于0.3时可以认为发动机已经发生了较为严重的退化,所提出的融合评估方法要优于传统的性能参数评估方法,能够提前预警气路性能严重退化。