航天器控制系统智能健康管理技术发展综述

健康管理作为智能自主控制亟待突破的关键技术之一,是提升航天器安全可靠稳定运行能力的有效手段。结合人工智能技术的发展趋势,基于前期已建立的新型航天器智能自主控制系统通用架构,详细综述航天器控制系统的智能健康管理技术现状与发展趋势。首先,根据现有航天器设计、研制和在轨的具体情况,梳理出航天器控制系统健康管理技术所面临的挑战;然后,分别从故障预警、故障诊断和寿命评估3个方面,详细阐述基于人工智能的健康管理技术研究现状及其在航天领域的应用情况;最后,提炼出航天器控制系统健康管理技术的发展方向。     

航天器在轨可更换模块机构与结构设计

航天器常在其任务中出现功能失效,造成巨大的损失,而在轨模块更换技术则是利用空间机械臂自主对可接受在轨服务航天器进行故障模块更换、升级、补充消耗品等。通过对在轨可更换模块在轨服务的任务、在轨可更换模块的组件构成,从而得出在轨可更换模块的机构设计方案,进而又对在轨可更换模块进行结构设计,利用 SolidWorks 软件建立该结构的三维模型,实物模型验证了其可行性。     

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。     

一种基于投影寻踪的航天器健康评估方法

针对常用健康评估方法在航天器测控管理领域应用过程中出现的通用性、易用性和适用性方面的不足,提出了一种基于投影寻踪评估模型的航天器健康评估方法.该方法首先计算得出分系统运行状态的可信度;然后利用卫星故障预案和故障实例构建投影寻踪评估模型,自动获取运行状态指数评价权重;最后通过构建等级评价标准对航天器健康状态进行评估.以某在轨卫星为例进行仿真测试,验证结果显示该方法所构建模型简便且易于升级维护,评估结果与实际情况吻合,对解决实际的工程问题具有实用价值.     

类天宫航天器迎风面积建模及变化特性分析

针对类天宫航天器复杂结构外形迎风面积的精确计算问题,提出了一种在轨飞行期间迎风面积的计算模型。该模型考虑航天器姿态变化和帆板转动因素,在航天器本体系下建立经纬度网格,将三维的投影方向矢量转化为二维的经度、纬度;基于图形学几何投影方法,对投影经度、投影纬度、帆板转角模型参数按照一定颗粒度离线穷举,计算各种参数状态的迎风面积,离线建立航天器迎风面积模型;轨道计算过程中,根据航天器在轨飞行模式确定该时刻的姿态和帆板转动角度,通过迎风面积模型插值获得迎风面积。应用该方法计算天宫一号长期在轨飞行阶段的迎风面积,对不同时间尺度的变化规律进行了分析,验证了方法的有效性。     

低轨载人航天器原子氧环境仿真分析技术

原子氧是影响低地球轨道航天器在轨性能的重要空间环境因素之一,其氧化性对航天器表面材料及舱外组件造成损伤,严重影响航天器在轨寿命。针对此问题提出了一种原子氧仿真分析方法,以此对长期低轨运行的载人航天器模型在轨期间表面遭受到的原子氧累积通量进行分析,仿真结果与NASA实测飞行数据比较,二者吻合很好。本仿真计算方法可用于低轨飞行器原子氧防护设计,仿真结果可作为试验依据。     

基于ARIMA模型的载人航天器氧分压分析及预测方法

载人航天器环控生保系统中的氧分压分析监测关系到航天员在轨的安全与健康,是地面控制中心重点关注的关键信息。提出了一种基于时间序列数据模型的载人航天器氧分压分析及预测方法,对在轨遥测数据进行处理分析,应用ARIMA模型对氧分压历史数据进行分解建模,并预测其未来趋势。通过对载人航天器氧分压在轨数据的实测分析,对历史数据拟合均方根误差为0.1537 kPa,预测均方根误差为0.1378 kPa,预测精度较高。该方法基于短期历史环境信息分析建模,实现对未来状态变化的有效预测,有效提升了现有预测方法的预测时长,提前识别系统运行过程中的异常状态。     

航天器自主导航状态估计方法研究综述

自主导航是航天器自主运行的核心关键技术。状态估计是实现航天器自主导航的核心手段,是指实时确定航天器在轨位置、速度和姿态等导航参数,是航天器自主导航技术的重点发展方向之一。首先,针对航天器自主导航的实际需求,阐述了研究航天器自主导航状态估计方法的必要性,具体从导航系统可观测性分析、导航滤波算法、导航系统误差补偿3个方面介绍了航天器自主导航状态估计方法的研究现状;然后,分析并总结状态估计方法在航天器自主导航系统中的实际应用;最后,结合理论研究和实际应用,给出了状态估计方法目前存在的主要问题并对其后续发展进行了展望。     

面向航天器在轨装配的数字孪生技术

构建航天器在轨维修维护能力是确保空间系统长期稳定工作的有效途径,而对于空间环境中的在轨装配过程的模拟、监控、诊断和预测,目前的研究尚处于探索阶段,研究成果相对较少且缺乏整体解决方案。提出采用构建航天器数字孪生体的方式,来抽象表达航天器完成在轨装配的过程、状态和行为。首先分析了在轨装配航天器的结构组成及功能需求,然后系统阐述了航天器数字孪生体的数据组成、实现方式和作用,最后给出了航天器数字孪生体在设计、制造和在轨服务阶段的实施途径,并对航天器数字孪生体的作用进行了总结和展望。     

面向大型载人航天器的系统健康状态评估方法研究

针对载人航天器系统在安全运行方面的需求,提出了一种面向大型载人航天器的系统健康状态评估方法。首先根据复杂系统的拓扑结构建立图模型,将对系统的健康状态评估转化为对图模型各成分的度量;然后挖掘出图模型中各成分关联数据的模式特征,以系统稳态时的模式特征作为健康基准,将后续观测时刻模式特征与基准模式之间的偏差作为健康状态的度量值,着重考虑了系统组成部分之间的相互作用;最后综合图模型中所有成分的度量结果,构建出表征整个系统健康状态的评估值。以某型号航天器的能源系统在轨遥测数据进行了验证分析,结果表明所提出的方法可以有效地完成对复杂系统的健康状态评估工作,可以为大型载人航天器的健康状态评估提供参考。     

基于分布式IMA平台的系统健康管理的设计与实现

随着分布式综合模块化航电（分布式IMA）系统的深入应用,需要针对分布式IMA平台的健康监控、故障管理、系统容错重构、系统运行日志等健康管理能力进行进一步研究。本文深入研究了 VxWorks653操作系统中的健康监控机制和ASAAC标准中的系统管理技术,以此为基础设计并实现了分布式IMA平台级和系统级的健康管理方案。在本文的方案中,对 VxWorks653操作系统的健康监控机制进行改进,使其能够满足自定义的故障处理需求,将ASAAC标准中的系统管理技术和IMA系统内故障信息的收集相结合,实现了对IMA系统内各进程、各分区、各模块乃至整个IMA系统进行健康管理。     

基于粒化模糊熵的机载燃油泵故障诊断

由于机械系统的复杂性,机载燃油泵振动信号的随机性表现在不同尺度上,因此需要对振动信号进行多尺度分析。为了实现机载燃油泵的故障状态特征提取,以模糊熵作为机载燃油泵振动信号的基本特征,提出了基于模糊信息粒化和模糊熵的机载燃油泵故障诊断方法。首先,采用模糊信息粒化方法对振动信号进行粒化处理,得到包含最小值、中值、最大值三组模糊信息粒;其次,计算模糊信息粒的模糊熵值;最后,将熵值作为特征向量,输入基于粒子群优化支持向量机建立的分类器。将该方法应用于机载燃油泵及轴承实验数据,分析结果表明,该方法可有效实现故障诊断。     

基于样本分位数的机载燃油泵故障状态特征提取及实验研究

机载燃油泵的健康状态关系着飞行任务的完成和飞行安全,对机载燃油泵的故障状态特征提取及诊断成为亟需解决的问题。通过对机载燃油实验系统的振动与压力信号进行综合分析,提出了一种基于样本分位数的故障状态特征提取方法。首先,根据样本分位数的渐近分布定理,讨论了样本分位数的统计特性,分析了故障状态与样本分位数的对应关系,从理论上保证了该方法的可行性,在实测数据统计分析的基础上,讨论了样本容量对样本分位数稳定性的影响;其次,根据样本分位数渐近分布定理计算各故障状态的置信区间,并与Bootstrap方法得到的置信区间进行对比,结果显示,依据样本分位数渐近分布定理得到的置信区间真实可靠,为在线故障诊断提供了依据;然后,以各故障状态下提取的样本分位数为特征向量构建贝叶斯判别函数,进行故障诊断;最后依据故障诊断的正确率对传感器进行优化,结果表明,同时安装振动传感器与压力传感器可以提高故障诊断的正确率,并且只安装1个压力传感器与1个特定方向的振动传感器即可对机载燃油泵的故障状态进行完全识别。为快速准确的在线判断机载燃油泵的状态提供了理论支撑,并且可以降低工程应用中机载燃油泵监测系统的体积、功耗及复杂性。     

Fault processing policy based on dual-redundant control law for aero-engine control sensors

A fault processing policy was proposed for the circumstance under which all the dual-redundant closed-loop feedback control sensors went wrong. This policy was based on dual-redundant control law and may maintain the performance of turbofan engines. When pair of controlling sensors went wrong and the primary control law was unable to implement, control system performs the backup control law instead of the primary one so that the fault sensors were isolated from the closed control loop. This fault processing policy may not only avoid increasing engine weight and cost with the additional sensors hardware, but also avoid the error of analytical sensor signal. It can improve the engine mission reliability and control quality.     

Fault self-repair strategy based on evolvable hardware and reparation balance technology

In the face of harsh natural environment applications such as earth-orbiting and deep space satellites, underwater sea vehicles, strong electromagnetic interference and temperature stress,the circuits faults appear easily. Circuit faults will inevitably lead to serious losses of availability or impeded mission success without self-repair over the mission duration. Traditional fault-repair methods based on redundant fault-tolerant technique are straightforward to implement, yet their area, power and weight cost can be excessive. Moreover they utilize all plug-in or component level circuits to realize redundant backup, such that their applicability is limited. Hence, a novel selfrepair technology based on evolvable hardware（EHW） and reparation balance technology（RBT） is proposed. Its cost is low, and fault self-repair of various circuits and devices can be realized through dynamic configuration. Making full use of the fault signals, correcting circuit can be found through EHW technique to realize the balance and compensation of the fault output-signals. In this paper, the self-repair model was analyzed which based on EHW and RBT technique, the specific self-repair strategy was studied, the corresponding self-repair circuit fault system was designed, and the typical faults were simulated and analyzed which combined with the actual electronic devices. Simulation results demonstrated that the proposed fault self-repair strategy was feasible. Compared to traditional techniques, fault self-repair based on EHW consumes fewer hardware resources, and the scope of fault self-repair was expanded significantly.     

临近空间无人飞行器多余度容错导航系统设计

临近空间无人飞行器导航系统的故障直接影响到飞行器的任务执行和飞行安全,因此必须能够长时间地保持稳定性和精确性,为达到此目的必须设计由惯性导航、卫星导航等多种导航传感器组成的多源多余度容错导航系统,提高系统的可靠性。针对临近空间飞行器制导控制对导航信息的需求,提出了一种标准的三余度导航系统架构,并设计了采用新型加权平均表决子算法,具备故障检测和隔离以及故障重构功能的容错重构算法,构建了适用于临近空间无人飞行器的多余度容错导航系统,通过实测试验数据仿真验证了容错导航系统的性能,展示了系统一次故障工作的故障容错能力。所研究内容也可被其他类型的无人飞行器借鉴和参考。     

Psychological health maintenance on Space Station Freedom

The scheduling of crew rotations for up to 180 days on Space Station Freedom presents a special challenge for behavioral scientists who are tasked with providing psychological support for the crews, their families, and mission flight controllers. Preflight psychological support planning may minimize the negative impact of psychological and social issues on mission success, as well as assist NASA management in making real-time mission planning decisions in the event of a significant social event (for example, the death of a family member). During flight, the combined psychological, emotional, and social stressors on the astronauts must be monitored, along with other aspects of their health. The Health Maintenance Facility (HMF) will have the capability of providing preventive, diagnostic, and therapeutic assistance for significant psychiatric and interpersonal problems which may develop. Psychological support will not end with the termination of the mission. Mental health professionals must be part of the team of medical personnel whose job will be to facilitate the transition--physical and mental--from the space environment back to planet Earth. This paper reviews each phase of mission planning for Space Station Freedom and specifies those factors that may be critical for psychological health maintenance on extended-duration space missions.     

A novel testability model for health management of heading attitude system

Prognostics and health management (PHM) is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite’s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.     

Sensor Optimization Selection Model Based on Testability Constraint

Sensor selection and optimization is one of the important parts in design for testability. To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability, a novel sensor optimization selection model is proposed. Firstly, a universal architecture for sensor selection and optimization is provided. Secondly, a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability. Thirdly, a sensor selection and optimization model for prognostics and health management is constructed, which takes sensor cost as objective function and the defined testability indexes as constraint conditions. Due to NP-hard property of the model, a generic algorithm is designed to obtain the optimal solution. At last, a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform. The application results and comparison analysis show the proposed model and algorithm are effective and feasible. This approach can be used to select sensors for prognostics and health management of any system.