载人航天中人的失误分析及研究对策

文章首先将载人航天中产生人失误的原因分为三大类型：载人航天器系统人机界面本身设计不当、生产加工与装配错误以及由人自身的局限性所引发的操作错误。在此基础上，详细论述了为防止航天员发生失误而采取的研究对策。最后，提出几点建议以供讨论。     

基于小波神经网络的模拟电路故障诊断

介绍了模拟电路故障诊断的神经网络方法及小波神经网络,以一带通滤波器为例,提出了一种基于输出灵敏度分析,利用多频测试生成故障特征向量训练小波神经网络进行故障诊断的方法。仿真结果表明小波神经网络作为故障分类器具有收敛速度快,诊断准确等特点。     

数据文本文件数字信号特征的提取

针对海量数据进行特征提取是物体目标识别的关键之一.本文编写了应用程序和操作算法程序,提取光学测光谱仪海量数字信号的特征.它是通过应用VC 构造人机用户界面,提取了光学测光谱仪中的有用数据之后,对数据进行预处理和期望、方差、k阶距、极值等参数计算,并且编制绘图程序,绘出各种参数数据曲线图,最终提取了海量数据的数字信号特征.文中以某实验数据为例进行了信号特征提取,验证了操作算法和应用程序的可行性,为卫星物体目标识别提供了一种方便、有效的方法.     

分布式可重构容错计算机设计技术的探讨

对分布式可重构容错计算机系统用于空间站的设计技术作了粗略探讨。其中主要包括:用分布、并行和重构方法提高可靠性和吞吐量,以及可复用软件库和开发工具的应用技术。     

多状态气路分析法诊断发动机故障的分析

对多状态气路分析法诊断发动机故障的可行性进行了分析。估值平均误差J是判断诊断精度的有效判据。通过数值试验给出了J值的建议值。就状态数的选择和监视参数的选择对诊断效果的影响进行了分析。通过分析指出适当增加和正确选择监视参数可减少状态数, 增加诊断精度。增加高压压气机后压力和高低压涡轮间的压力两个测量参数是实用的改善诊断精度的方法。      

小行星形貌特征的分析与描述

小行星形貌特征的分析对深空探测器的导航、着陆点的选取具有重要意义。现有的深空星体形貌特征的分析与描述主要集中在火星、月球等类地天体上,而作为宇宙中为数众多、信息量丰富的小行星却鲜有文献对其形貌特征作详细深入的介绍。文章以Vesta、Eros、Mimas等人类已探测的小行星为例,分析了几种典型的小行星表面形貌特征,完善了凹坑、岩石等形貌特征的描述参数,并用仿真实验生成了Mimas小行星表面的Herschel凹坑模型。实验结果显示,所提出的特征描述方法具有较好的仿真度和实用性。     

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.     

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control(FTC) for the medium-scale Unmanned Autonomous Helicopter(UAH) in the presence of external disturbances,actuator faults and input saturation.To improve the disturbance rejection capacity of the UAH system in actuator healthy case, an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system.Meanwhile, compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation.Based on the backstepping control and inner-outer loop control technologies, a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence.Under the presented FTC controller, the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis.Simulation results demonstrate the effectiveness of the proposed control algorithm.     

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.     

基于人工神经网络的多模型目标跟踪算法

针对在目标跟踪中单模型跟踪算法难以应对目标运动形式的变化,而多模型跟踪算法存在结构固定、跟踪精度被非匹配模型削弱且模型切换缓慢的矛盾,文章提出了一种基于人工神经网络的多模型目标跟踪算法。通过分析目标几种基本运动模式的轨迹特点,归纳出目标运动轨迹的特征向量。利用训练好的BP神经网络对滑窗里的轨迹段进行运动模型识别,按结果进行跟踪模型切换,达到使跟踪算法实时适应目标运动状态的目的。仿真结果证明了该算法的有效性,且与传统的多模型算法相比,具有结构更加简单、更强的灵活性和拓展性的特点。