锯齿型单壁碳纳米管的广义层错能计算

基于密度泛函理论的第一性原理方法,计算了两种不同半径的锯齿型单壁碳纳米管以及单层石墨的广义层错能曲线。对小位移处广义层错能曲线进行斜率拟合所得切变模量与其它文献值一致。对三条广义层错能曲线的对比可知,锯齿型碳纳米管与单层石墨的广义层错能曲线相近,锯齿型单壁碳纳米管的曲率效应不明显。     

强流脉冲电子束辐照下单晶铝中的堆垛层错四面体

利用强流脉冲电子束技术对单晶铝进行了辐照,并利用透射电镜对强流脉冲电子束诱发的空位簇缺陷进行分析.实验结果表明,强流脉冲电子束能够诱发位错圈、孔洞甚至堆垛层错四面体这种通常在高层错能金属中不能形成的空位簇缺陷,并且三种不同类型的空位簇缺陷的形核过程并不同时发生,三种空位簇缺陷存在着密切的关系.根据实验结果提出了堆垛层错四面体形成与生长机理. 关键词： 强流脉冲电子束 堆垛层错四面体 单晶铝 空位簇缺陷     

基于Polytec激光测振仪和第二代小波的齿轮箱故障诊断

齿轮箱是机械设备中一种必不可少的通用零部件,也是故障多发部件,而齿轮失效又是诱发其故障的重要因素。因此发展新型齿轮箱故障诊断技术具有重要意义。搭建了基于Polytec激光测振仪的光学测量系统,拾取齿轮箱振动信号,采用第二代小波提取故障特征,达到了故障诊断的目的,可用于齿轮箱故障预知和智能维修。     

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.     

A PCA Based Fault Detection Scheme for an Industrial High Pressure Polyethylene Reactor

A data‐based monitoring scheme is proposed to detect decomposition in low density polyethylene reactors by combining principal component analysis with a priori information on the heat balance equations around the reactor. During normal operating conditions, the heat balance equation should close at all times within reasonable limits. If excess heat is generated in the reactor, the heat balance closure error will exceed a user specified threshold limit to indicate the possible onset of decomposition. However, since precise information required to formulate the exact energy balance equations was not available, principal component analysis (PCA) was used as a model identification tool. Results from a number of decompositions case studies from an industrial low density polyethylene/ethylene vinyl acetate autoclave reactor indicate that the method was able to detect the onset of decomposition with reasonable lead time.

     

Fault‐tolerant sampled‐data mixed ℋ∞ and passivity control of stochastic systems and its application

This article addresses the problem of fault‐tolerant sampled‐data mixed and passivity control for a class of stochastic system with actuator failures, where the plant is modeled as a continuous‐time one and the control inputs are implemented as discrete‐time signals. Sufficient conditions for the reliable sampled‐data mixed and passivity performance control law is established for the considered systems by constructing an appropriate Lyapunov–Krasovskii functional together with the Newton–Leibniz formula and free‐weighting matrix technique. More precisely, linear matrix inequality based sampled‐data methodology is employed to design the mixed and passivity formation controller to reject the impact of the formation changes being treated as disturbances. Simulation studies are performed based on the flight control model to verify the stability, performance, and effectiveness of the proposed design strategy. © 2015 Wiley Periodicals, Inc. Complexity 21: 420–429, 2016     

Stabilization of active fault‐tolerant control systems by uncertain nonhomogeneous markovian jump models

This article investigates the stabilization and control problems for a general active fault‐tolerant control system (AFTCS) in a stochastic framework. The novelty of the research lies in utilizing uncertain nonhomogeneous Markovian structures to take account for the imperfect fault detection and diagnosis (FDD) algorithms of the AFTCS. The underlying AFTCS is supposed to be modeled by two random processes of Markov type; one characterizing the system fault process and the other describing the FDD process. It is assumed that the FDD algorithm is imperfect and provides inaccurate Markovian parameters for the FDD process. Specifically, it provides uncertain transition rates (TRs); the TRs that lie in an interval without any particular structures. This framework is more consistent with real‐world applications to accommodate different types of faults. It is more general than the previously developed AFTCSs because of eliminating the need for an accurate estimation of the fault process. To solve the stabilizability and the controller design problems of this AFTCS, the whole system is viewed as an uncertain nonhomogeneous Markovian jump linear system (NHMJLS) with time‐varying and uncertain specifications. Based on the multiple and stochastic Lyapunov function for the NHMJLS, first a sufficient condition is obtained to analyze the system stabilizability and then, the controller gains are synthesized. Unlike the previous fault‐tolerant controllers, the proposed robust controller only needs to access the FDD process, besides it is easily obtainable through the existing optimization techniques. It is successfully tested on a practical inverted pendulum controlled by a fault‐prone DC motor. © 2016 Wiley Periodicals, Inc. Complexity 21: 318–329, 2016     

故障树模块化在装甲车辆灭火系统故障诊断中的应用

为了实现装甲装备灭火系统故障的快速诊断,提出了一种故障树模块化分析方法;对灭火系统故障树进行深度优先最左遍历,并记录遍历过程,按照遍历顺序对故障树中的每个事件进行标定,并将灭火系统故障树划分为相互独立的模块,依据划分的模块可以通过故障现象对模块内的故障进行排查及修复;实验结果分析表明,该方法可以快速修复模块故障,恢复系统功能,简化了以往对灭火系统所有子事件遍历查错的繁琐过程。该方法同样可以计算故障模块的失效概率,并可以实现故障模块的整体更换,恢复系统性能;证明了故障树模块化方法在灭火系统故障诊断中具有较高的效率,简化了灭火系统诊断流程,在装甲车辆其他系统故障诊断中具有借鉴作用,符合现代作战对于装备保障的需求。     

基于极限学习机与规则推理的NPC三电平逆变器二级故障诊断方法

针对NPC三电平逆变器故障诊断问题,提出一种基于极限学习机与规则推理的二级故障诊断方法。分析了依据输出电流诊断故障的可区分性,以及故障模式的分类。然后对输出电流提取故障特征,并采用极限学习机完成故障初级分类。对于初级分类结果为电流不可区分故障情况,再根据桥臂电压信息运用规则推理法实现故障二级精确诊断。诊断实验表明,该方法能够实现NPC三电平的多模式故障诊断,且故障诊断方法简单、定位精确、快速、鲁棒性强。     

相对主元分析在船舶机舱监控系统的应用

目前船员对于船舶状况、航行状态等的判断主要还是依靠经验,难免会出现不恰当的决策,造成不必要的人力、财力的损失;针对这一问题提出并开发了一套融入相对主元分析船舶机舱监控系统;该系统除了具有常规机舱监控系统功能外,还能实现故障监测功能;协助管理人员进行系统故障的分析,从而更好地保证船舶航行的安全与效率,减少人为误操作带来的事故问题;增强了船舶监控系统的功能,大大提高了人员的工作效率减轻了人员的劳动强度;其设计满足船级社的定期无人机舱值守操作,为机舱监控系统的设计提供了一种新的思路。