基于规则和故障树的故障案例库构建方法研究

基于案例推理的故障诊断方法适用于经验丰富的决策环境,在飞机维修保障领域应用广泛,但由于缺乏解析模型和诊断规则的支持,难以满足复杂故障诊断要求。而故障树分析法建立在设计原理的基础上,可信度高。为了提高故障诊断效率和准确性,以实现基于规则和故障树模型与基于案例推理相融合的诊断方法为目的,研究案例的组织和案例库的构建。根据飞机维修保障实际需求,采用基于故障树分析的方法,结合故障诊断推理规则,建立飞机故障诊断模型;将模型和经验知识相结合,提出一种基于规则和故障树的案例库构建方法,进一步完善基于案例的推理机制,提高故障诊断的精确性和有效性。该案例库已应用于飞机故障诊断系统,实现了交互式故障诊断和排故引导,验证了研究方法的可行性。     

航天器专家故障知识模型的建立和应用

随着我国航天事业的不断进步和国际业务的拓展,在轨航天器的数量逐年增加,航天器长期有效的管理对保障航天器长寿命运行具有十分重要的意义。传统的地面监测人员对航天器的监视已经不能满足要求。本文介绍了一种多航天器专家故障知识模型的建立方法,简要说明了基于这种模型的应用可以增加航天器在轨管理的智能化,提高工作效率。     

基于单故障叠加的航天器故障数据仿真方法

针对测试故障诊断系统可靠性的问题,在传统基于对故障航天器的参数估计模拟的故障数据仿真基础上,提出一种基于单故障叠加模型的航天器复杂故障数据仿真方法,新型数据仿真方法能够在有限次迭代的情况下逐次逼近仿真航天器故障数据,对航天器故障的仿真实验证明,该方法可广泛应用于各类航天器故障诊断系统的测试过程。     

基于航天器故障树的过程性诊断方法研究

针对航天器故障过程性推理的实际需求,提出采用故障树的节点式知识表示方法和过程推理机制处理航天器过程性知识和陈述性知识,同时为实现对航天器故障的诱因定位和早期预测功能,设计了故障树双向混合推理算法。     

航天器智能诊断系统开发研究

提出了基于故障树的故障报警方法和诊断方法,提出了面向故障树的报警知识和诊断知识表示方法及相应的报警和诊断推理策略,最后,在Windows环境下,用BorlandC++实现了一个原型系统,并通过对“实践-四号”卫星能源系统故障模拟实验台的诊断验证了系统的有效性。     

基于仿真系统的航天器故障监视软件设计

围绕在航天飞行控制中心（AFCC）建立基于仿真系统的、与任务实战同步的航天器故障监视软件这一主题,简介系统环境,论述仿真系统航天器同步故障监视软件的软件需求,分析和设计软件体系结构,并给出部分实现的示例。     

航空发动机内窥故障诊断专家系统研究

采用BP神经网络准确识别内窥故障信息,并且建立综合基于规则推理和基于案例推理的混合专家系统,对内窥故障进行智能诊断。实验结果证明,该专家系统能快速有效地给出损伤评估和维修策略。     

飞机电液伺服阀早期故障征兆诊断

电液伺服阀是飞机刹车系统的重要执行部件,影响着飞机的着陆安全。针对某型飞机电液伺服阀故 障多发的问题,运用一元线性回归分析方法构建刹车系统电液伺服阀的分析模型,根据分析模型和故障征兆判 据对电液伺服阀近期的工作数据进行分析和故障征兆诊断。结果表明:基于回归分析的故障诊断方法可以准 确地诊断电液伺服阀存在的故障征兆;早期故障征兆诊断可以预先发现电液伺服阀潜在的故障隐患,降低故障 发生率,显著提高飞机刹车系统的安全性和可靠性,也有助于优化维修策略和降低飞机全寿命维护成本。     

基于模型的航天器推进系统故障诊断

本文设计了基于模型的推进系统的故障诊断系统。根据推进系统的结构和行为模型,分析出各组件所处的工作状态及组件之间的连接关系,利用JMPL建模语言建立了推进系统各组件的定性模型。把推进系统模型和系统场景文件输入给诊断推理引擎,可实现推进系统的实时诊断。诊断结果表明,建立的推进系统模型是准确可靠的,开发的诊断系统能有效地找出故障组件并确定故障组件的状态。     

航天器自动化测试语言研究

航天器测试语言是支撑航天器自动化测试的形式体系及航天器测试过程标准,在当前多航天器批产网络化测试的新需求下,测试语言标准体系研究对于提高航天器测试自动化水平和保障测试过程安全具有重要意义。通过对现有典型航天器测试语言的全面分析和比较,总结出基本特征,并结合当前先进的网络计算技术,提出了我国航天器测试语言发展的目标和方向,同时针对国内航天器测试语言研究设计工作的不足,给出一种航天器测试语言CATOL(China Aerospace Test and Operation Language)。该研究对提高我国航天器测试业务规范水平和测试人员的工作效率、促进航天器测试自动化研究的发展将起到一定的推动作用。     

Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros

This paper proposes a neural network-based fault diagnosis scheme to address the problem of fault isolation and estimation for the Single-Gimbal Control Moment Gyroscopes(SGCMGs) of spacecraft in a periodic orbit. To this end, a disturbance observer based on neural network is developed for active anti-disturbance, so as to improve the accuracy of fault diagnosis.The periodic disturbance on orbit can be decoupled with fault by resorting to the fitting and memory ability of neural network. Subsequ...     

载人航天器推进系统健康监测的组件技术研究

为了适应载人航天器推进系统多发动机的分布式结构,提高健康监测系统的可靠性,应用组件技术构造了分布式多智能组件集成的健康监测系统,给出了组件之间的协调修正和决策算法。原型系统仿真结果表明,该技术比传统的面向对象（ＯＯＰ）技术更适用于分布式系统,可应用于载人航天器推进系统的健康监测。     

航天器推进系统故障的面向时态检测和诊断

根据航天器推进系统的时变性特点，在规则的专家系统方法基础上，提出了推进系统面向时态的故障检测和诊断方法，在对知识表示方法和推理诊断策略进行改进后，在Windows NT环境与实现了一个基于知识的故障诊断原型系统，介绍了原型系统的结构、功能及网络实时数据通信方法，经过仿真数据测试验证了方法的有效性。     

航空发动机整机振动故障模糊信息熵诊断方法

为了获得航空发动机整机振动故障征兆与故障原因之间难以确定的复杂隶属关系,提高故障诊断的准确率,基于模糊数学理论和信息熵理论,提出了基于模糊信息熵的发动机整机振动故障融合诊断方法。通过建立模糊信息熵融合诊断的数学模型,使定量分析与专家经验和定性分析相结合,并应用于某型航空发动机整机振动故障诊断。实际应用表明,基于模糊信息熵融合的诊断模型较单一的模糊诊断模型准确率明显提高,验证了该方法用于航空发动机整机振动故障诊断是有效的。     

航天器故障诊断和预测天地一体化集成系统

在轨航天器和地面航天飞控中心天地2个系统缺乏相互支持和配合,是导致对航天器故障诊断和预测能力不足的重要因素之一。为了提高航天器故障诊断和预测的有效性、可靠性和准确性,需要将天地2个系统集成为一个整体（天地一体化系统）。设计了航天器故障天地一体化集成诊断和预测系统,讨论了航天器故障天地一体化集成诊断和预测方法,对航天器故障天地一体化集成诊断系统和方法的特点进行了分析。将天地2个系统集成在一起对航天器故障进行诊断和预测,其效率、准确性和可靠性较高。     

Event-triggered adaptive control for attitude tracking of spacecraft

Plug-and-play technology is an important direction for future development of spacecraft and how to design controllers with less communication burden and satisfactory performance is of great importance for plug-and-play spacecraft. Considering attitude tracking of such spacecraft with unknown inertial parameters and unknown disturbances, an event-triggered adaptive backstepping controller is designed in this paper. Particularly, a switching threshold strategy is employed to design the event-triggering mechanism. By introducing a new linear time-varying model, a smooth function, an integrable auxiliary signal and a bound estimation approach, the impacts of the network-induced error and the disturbances are effectively compensated for and Zeno phenomenon is successfully avoided. It is shown that all signals of the closed-loop system are globally uniformly bounded and both the attitude tracking error and the angular velocity tracking error converge to zero. Compared with conventional control schemes, the proposed scheme significantly reduces the communication burden while providing stable and accurate response for attitude maneuvers. Simulation results are presented to illustrate the effectiveness of the proposed scheme.     

Shape control of spacecraft formation using a virtual spring-damper mesh

This paper derives a distance-based formation control method to maintain the desired formation shape for spacecraft in a gravitational potential field. The method is an analogy of a vir-tual spring-damper mesh. Spacecraft are connected virtually by spring-damper pairs. Convergence analysis is performed using the energy method. Approximate expressions for the distance errors and control accelerations at steady state are derived by using algebraic graph representations and results of graph rigidity. Analytical results indicate that if the underlying graph of the mesh is rigid, the convergence to a static shape is assured, and higher formation control precision can be achieved by increasing the elastic coefficient without increasing the control accelerations. A numerical exam-ple of spacecraft formation in low Earth orbit confirms the theoretical analysis and shows that the desired formation shape can be well achieved using the presented method, whereas the orientation of the formation can be kept pointing to the center of the Earth by the gravity gradient. The method is decentralized, and uses only relative measurement information. Constructing a distributed virtual structure in space can be the general application area. The proposed method can serve as an active shape control law for the spacecraft formations using propellantless internal forces.     

Target localization method of non-cooperative spacecraft on on-orbit service

With the explosion of the number of meteoroid/orbital debris in terrestrial space in recent years, the detection environment of spacecraft becomes more complex. This phenomenon causes most current detection methods based on machine learning intractable to break through the two difficulties of solving scale transformation problem of the targets in image and accelerating detection rate of high-resolution images. To overcome the two challenges, we propose a novel non-cooperative target detection method using the framework of deep convolutional neural network.Firstly, a specific spacecraft simulation dataset using over one thousand images to train and test our detection model is built. The deep separable convolution structure is applied and combined with the residual network module to improve the network’s backbone. To count the different shapes of the spacecrafts in the dataset, a particular prior-box generation method based on K-means cluster algorithm is designed for each detection head with different scales. Finally, a comprehensive loss function is presented considering category confidence, box parameters, as well as box confidence. The experimental results verify that the proposed method has strong robustness against varying degrees of luminance change, and can suppress the interference caused by Gaussian noise and background complexity. The mean accuracy precision of our proposed method reaches 93.28%, and the global loss value is 13.252. The comparative experiment results show that under the same epoch and batchsize, the speed of our method is compressed by about 20% in comparison of YOLOv3, the detection accuracy is increased by about 12%, and the size of the model is reduced by nearly 50%.     

Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm

This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.