发动机故障的数值模拟及模式分析

针对发动机的邦联特点，提出了其工作状态过渡的非线性动态数学模型。利用随机模拟结果，对发动机的邦联特性进行了简单分析。     

竞争学习模糊神经网络及在导弹故障诊断中的应用

尽管基于解析冗余的故障诊断方法有许多突出的优点而越来越多地得到的研究和应用，但它依赖于系统的模型，当系统存在非线性或不确定性时，存在难以建模的困难，模糊神经网络可以通过学习建立系统的模型，且模型参数有明确的物理意义，初始参数易于选择，成为解决这一问题的优选途径，作者通过把模糊神经网络的学习转化为竞争聚类和线性优化问题，基于竞争聚类和最小二乘原理，提出了一种模糊神经网络学习算法，并在某伺服机构上进行     

基于导航定位原理的火箭涡轮泵轴承故障诊断

为准确判断火箭发动机涡轮泵轴承在试验台上试验时发生故障的部位，同时避免通过轴承特征频率诊断轴承故障的方法所带来的不确定性，基于GPS卫星导航定位原理，利用时幅曲线的相位信息，提出一种新的轴承故障诊断方法:振源坐标定位法，即通过四个已知坐标的振动传感器测得同一振动波的时幅曲线相位差判定振源位置。将试验台上轴承和四个振动信号传感器安放在坐标已知的直角坐标系中，利用时幅曲线拐点分析法准确捕获振动信号到达四个传感器的时刻，再利用这四个时刻和已知坐标计算出振源位置坐标，最后根据振源位置坐标判断其是否为轴承故障及具体故障部位。通过仿真计算证明该方法理论上可行。     

在轨卫星异常报警和故障诊断方法研究

及时发现卫星出现的异常是卫星在轨管理的一项重要工作，它关系到卫星在轨使用寿命。能够及时发现并判断卫星异常的方法有很多，本文对这些方法进行了研究，并将其归纳为2类，一类是基于遥测参数超限报警方法，另一类是故障诊断方法。分别对这2类方法从功能、效果、软件实现的难度进行比较，结果表明，在不同的管理和实现难度要求下，这些方法完成卫星管理的效果不同。另外，本文还提出了一种遥测参数相对判断算法，简化了报警门限设置，能够及时发现卫星任何参数突跳，是一种有效的卫星异常报警方法。     

《盐铁论》反义词研究

反义词是同一语言、同一历史时期,在一定的语境中具有对举关系,在一个或几个义位上相对或相反而词性相同的词。《盐铁论》中不仅反义词数量丰富,而且在语法类型、词汇意义和对应关系方面也显示出一些独具的特征。其语法特征表现为词类简单,形容词最多;词义方面涉及范围广,有时间、空间、动作以及性质状态等多种关系的对立;对应关系方面主要是音节上的对称性和数量上的不平衡性。     

论精品课程建设应具有的共性

基于精品课程建设的意义、任务和目的,从综合性、示范性、公益性、规划性、针对性和创新可持续性等多侧面分析解读精品课程建设的共性。为各级各类教育精品课程建设正确理解和准确把握精品课程的精髓、树立精品意识。把精品课程打造成真正的精品,更好地服务于社会。     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

Decentralized sliding-mode control for spacecraft attitude synchronization under actuator failures

This paper examines attitude synchronization and tracking problems with model uncertainties, external disturbances, actuator failures and control torque saturation. Two decentralized sliding mode control laws are proposed and analyzed based on algebraic graph theory. Using Barbalat׳s Lemma, it is shown that the control laws guarantee each spacecraft approaches the desired time-varying attitude and angular velocity while maintaining attitude synchronization among the other spacecraft in the formation. The first controller is designed in the presence of model uncertainties, external disturbances, and actuator failures. The results are extended to the case with control input saturation in the second controller. Both control laws do not require online identification of failures. Numerical simulations are presented to show the effectiveness of the proposed attitude synchronization and tracking approaches.     

无网格方法中含参数指数权函数的研究

选择合适的权函数及相应的影响半径是无网格方法中很重要的问题,在引入数值紧支域概念的基础上分析了含参数的指数权函数中参数的选取范围,综合考虑计算效率和精度后给出了参数的选取原则,并讨论了在特定的参数下的最优半径以及不同参数对最优半径的影响.     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.