装备故障远程诊断服务系统的设计

针对一体化作战条件下我军电子对抗装备维护保障的需要，设计了装备故障远程诊断服务系统。根据系统的功能需求，描述了系统的设计模型，探索了解决问题的途径。     

基于案例的推理在导航装备故障案例库系统中的应用

在分析了基于案例推理的基本原理基础上,结合导航装备故障维修的实际情况,提出了基于案例推理的导航装备故障案例库系统的总体框架设计和案例推理运行流程.研究和开发实用、可靠、高效的导航装备故障案例库系统,对于导航装备的维修和保障具有重要意义.     

雷达对抗侦察接收机故障智能诊断系统神经网络构建与仿真

针对目前电子对抗装备快速故障检测与维护保障的需要,研究了某型雷达对抗侦察接收机故障智能诊断系统神经网络的建模方法,阐述了网络神经元的选取、网络结构的确定以及网络训练步骤等,并进行了网络训练仿真和网络实例测试,验证了系统故障诊断结果的可信性。     

现代雷达装备综合智能故障诊断系统设计

为克服传统雷达装备故障诊断技术的局限性,适应雷达装备维修保障的发展需求,结合现代雷达装备的结构和故障特点,设计了一种现代雷达装备综合智能故障诊断系统。该系统采用分层次分模块的故障诊断方式,将神经网络、专家系统和模糊推理等智能诊断技术有机结合。仿真结果表明,该系统能实现对复杂形式故障快速、精确地进行诊断。     

基于Smart Client模式的战时信息通信装备区域保障系统设计

由于信息通信装备的特殊性，战时有着严格的区域保障要求。为提高战时信息通信装备区域保障系统的综合能力，设计基于Smart Client模式的战时信息通信装备区域保障系统。基于Smart Client模式设计系统架构，在装备综合能力评估模块中构建战时信息通信装备综合能力评估模型。在故障预测诊断模块中，基于模拟退火粒子群算法与相关向量机建立信息通信装备的故障预测诊断模型。设计多个数据库表以存储各种系统数据，完成数据库模块的设计。系统测试结果表明，设计系统的查询反应时间低于2 000 ms，综合能力评估误差、装备区域故障预测诊断的RMSE均较低，可有效提高战时信息通信装备区域保障系统的综合能力。     

无人机装备自主式保障系统研究

目前我军无人机装备采用事后维修、定期维修、预防性维修等维修保障方式,存在故障诊断及修复能力差、维修资源繁多、保障费用急剧增长等问题。充分考虑我军无人机装备技术特点及现行维修保障体制,文中创新性地提出了构建适合我军无人机装备的自主式保障系统;并对系统组成、故障模式影响及危害性分析、测试性设计技术、机载故障快速诊断及自修复技术、预测与健康管理技术、分布式信息融合技术等关键技术进行重点研究。该系统为我军新型信息化装备提供了新的维修保障模式,并可提高我军无人机装备的一体化、精确化保障水平。     

陆军装甲指挥信息系统装备PHM应用技术研究

在分析了装甲指挥信息系统故障装备3级故障特点的基础上,从装甲指挥信息系统装备维修保障的实际需要出发,设计了装甲指挥信息系统装备PHM系统的体系结构和功能构成,并对各功能分系统进行了论述,对装甲指挥信息系统装备PHM系统的建设进行了有效的理论探索.     

基于HLA的装备保障全要素集成模拟训练系统设计

为提高装备保障训练效果,从装备保障全要素集成训练需求出发,设计了装备保障全要素集成模拟训练系统功能层次结构,设立了三种训练模式,合理区分了训练层次。基于HLA设计了模拟训练系统的体系结构,并提出了系统软件设计总体方案。最后系统阐述了装备保障全要素模拟集成训练系统的工作流程。该模拟训练系统可为装备装备保障全要素集成训练提供训练平台支撑。     

武器装备便携式维修辅助系统设计与实现

为了快速检测装备故障,提高装备维修效率,提出了装备便携式维修辅助系统的总体设计思路。运用系统集成技术进行一体化结构设计,构建了装备便携式维修辅助系统,实现了硬件平台多仪器一体化综合集成,并为系统扩展和二次开发预留了接口;采用VITE测试开发环境进行软件平台设计,建立IETM系统实现了装备辅助维修决策。实际应用表明,系统可在战场环境下实现装备的快速修复和远程技术支持,有助于提高部队的维修水平,降低维修保障费用,提高装备完好率。     

雷达接收机故障诊断辅助系统的设计研究

为了提高雷达接收机故障诊断的速度和精度,快速准确地排除接收机故障,设计出雷达接收机离线故障诊断辅助系统。该系统使用嵌入式微处理器智能控制,结合大规模集成电路现场可编程门阵列（FPGA）,可产生模拟雷达中频回波信号,使雷达接收机在仿真环境中工作,以便于维修人员进行故障检测分析。该系统已经应用于某型雷达检测仪。     

信标信号频谱在接收机跟踪性能分析上的应用

接收机锁定卫星信标后,经过二次或三次变频将C频段信标转变为70 MHz中频,然后通过解调单元从中频中提取误差信号。通过分析比较信标信号、L频段频率、70 MHz中频信号频谱使跟踪系统的性能指标层次化,从信标频谱中提取有用信息,全面判断数字接收机跟踪性能,对单脉冲跟踪体制下的接收机故障排查具有一定的指导意义。     

Receiver design for wavelet-based multicarrier CDMA communications

Wavelet packets have good properties such as orthogonality and multirate flexibility, and have resulted in a number of works for its applications to code-division multiple-access (CDMA) communications. In this paper we investigate the use of a special set of the wavelet packets, i.e., the waveforms formed from a full binary wavelet packet tree, in multicarrier CDMA systems. In the conventional multicarrier CDMA systems, a simple frequency-domain combining receiver is commonly used to achieve frequency diversity. To combat multipath channel effects, guard intervals are inserted between consecutive symbols. On the other hand, wavelet packets have the property of localization in both time and frequency domains. A novel receiver design utilizing this property to achieve both frequency and time diversity is proposed and evaluated in this paper. This receiver eliminates the need for guard intervals. Simulations are given to support the system and receiver design. This analysis can be extended to the more general wavelet packet basis waveform sets.     

基于故障树分析的雷达故障诊断专家系统设计

快速进行故障诊断是保证雷达装备综合保障能力的前提,建立一套故障诊断专家系统来不断总结专家经验,从而使维修工作更加科学、有效。以某舰载搜索雷达系统为例,给出了雷达系统故障树的构建方法、故障诊断专家系统知识库的建立及推理机的设计,并简要介绍其实现方法。     

无源功分网络与模数转换器对雷达接收系统噪声的影响

对无源功分网络及模数转换器(ADC)在雷达接收系统中的噪声影响做出详细的理论分析和公式推导,提出ADC在链路指标计算时的简化模型,并以直采链路为例,针对不同的工作状态和可能发生的故障情况,对含有无源功分网络及ADC的接收系统链路进行增益与噪声理论推导和测试验证,明确无源功分网络及ADC对系统噪声的影响与贡献,使得在 接收系统出现故障时,能够进行快速、准确的故障定位。     

Smart UWB System Based on STC and TR Techniques for BER Performance Enhancement and Interference Rejection

In this research, a novel smart UWB system is introduced. The proposed system is based on using an adaptive maximum ratio combining (MRC) Rake receiver. The proposed adaptive Rake receiver uses Genetic algorithm (GA) to adaptively select the delays of the fingers of the Rake receiver depending on the channel impulse response. It adaptively selects the delays that will allow the Rake receiver to capture most of the energy in the multipath components with minimum complexity. This adaptive Rake receiver is referred to as a GA Rake. The adaptive GA Rake is applied to a single-input single-output and space time coding (STC) multi-input single-output UWB systems. The performance of those systems using a GA Rake is compared to their performance when using a conventional MRC-Rake receiver and showed a great enhancement in performance with less receiver complexity. Also, in this paper, the smart UWB system using STC is modified by using the time reversal (TR) pre-coding technique. The modified system is referred to as a TR smart UWB system. This modification leads to more enhancements in performance and more reduction in receiver complexity over the smart UWB system. Moreover, this paper also shows the ability a TR smart UWB system in combating interference from other UWB systems.     

模糊贝叶斯网络模型在卫星导航时频系统可靠性分析中的应用

利用模糊贝叶斯网络方法对卫星导航系统的时间频率分系统的可靠性进行建模和分析.在对时间频率系统故障汇总分析的基础上建立时间频率系统的可靠性模型,并对引发故障的事件进行模糊描述.通过对时频系统发生故障的概率进行计算,可以对系统发生故障的风险进行评估.模糊贝叶斯网络模型提高了故障概率描述的精确性,对系统的可靠性分析更加准确可靠.仿真结果验证了该方法的正确性和可行性.     

中频接收机中潜在电路的分析与消除

通过对中频接收机中潜在电路现象的分析，提出在电子系统生产调试中，运用故障树分析（ＦＴＡ）法对捕捉到的潜在电路现象进行分析，找出电子设备产生潜在通路故障的部位，采取有效措施消除隐患，以提高电子系统可靠性和电子产品的质量。     

Online Fault Prediction Based on Combined AOSVR and ARMA Models

Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always offline that are not suitable for online and real-time processing. For the complicated nonlinear and non-stationary time series, it is hard to achieve exact predicting result with single models such as support vector regression （SVR）, artifieial neural network （ANN）, and autoregressive moving average （ARMA）. Combined with the accurate online support vector regression （AOSVR） algorithm and ARMA model, a new online approach is presented to forecast fault with time series prediction. The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes. Moreover, its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model. Fault prediction with combined AOSVR and ARMA can be realized better than with the single one. Experiments on Tennessee Eastman process fault data show the new method is practical and effective.