液体火箭发动机健康监控──故障检测与诊断

以大型液体火箭发动机故障诊断系统框架为基础，按照发动机不同工作阶段的特点建立了相应的故障检测与诊断算法。利用发动机的高队数学模型，对基于推广的卡尔曼滤波器技术的新息检测算法进行了研究。根据发动机系统工作过程的特点，建立了降阶故障模式响应模型，并发展了相应的故障模式检验方法。为了适应在线实时检测的需要，利用发动机的试验数据，分别研究了基于人工神经元网络辨识模型的发动机启动过程检测算法和基于时间序列分析方法的发动机主机工作过程检测算法。通过试验数据的检测验证，证明了这些实时算法的快速性、准确性和鲁律性。     

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。     

航天器的故障建模与应用

针对航天器工程中的典型故障进行分析与建模.首先给出几种典型故障作用模式的具体数学形式,然后分别采用状态描述矩阵对执行机构和传感器故障进行刻画,其不同的取值对应上述不同的故障模式.另外,使用有界范数和凸面体两种形式对系统故障数学建模.通过对航天器工程中的部件故障建模可为故障诊断和容错设计提供必要的数学描述.最后通过实例简...     

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.     

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.     

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。      

电控汽油喷射改善小型无人机发动机性能的研究

电控汽油喷射能有效地改善发动机的性能,特别对小型无人机发动机,可以实现空中飞行过程中对发动机各参数的实时控制。本文以试验研究为手段,介绍了电控汽油喷射在小型无人机发动机上应用研究的初步成果,然后从理论上分析了电控汽油喷射改善发动机性能的因素和潜力,并且强调了它在小型无人机发动机上应用的重要性。     

利用奉献观测器诊断红外地球敏感器故障的新方法

卫星闭环控制系统中不同敏感器的故障定位是自主故障诊断的难点。基于双观测器的方法能够分离光学敏感器和惯性敏感器的故障,却仍不能确定红外地球敏感器是否发生故障。奉献观测器适用于敏感器系统的故障定位,但由于该方法要求的能观性条件在实际工程中有时难以满足,使其应用受到限制。本文提出了一种利用奉献观测器诊断敏感器故障的新方法,先对不完全能观系统进行能观性分解,再对能观子系统设计奉献观测器,克服能观性条件的限制,实现敏感器故障诊断。最后,将该方法用于卫星红外地球敏感器的故障定位,仿真结果表明,该方法可有效诊断地球敏感器的故障。