两栖装甲装备故障诊断智能检测系统设计

针对目前两栖装甲装备故障检测时间长、检测效率低的缺点,研究设计了一种两栖装甲装备故障诊断智能检测系统。系统充分利用现代最新发展的大规模集成电路技术和数字处理技术,对多发、易发故障部位的故障信号进行采样、检测及处理,从而快速确定故障部位,便于及时排除故障。     

重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。     

医院局域网丢包实例解析

医院局域网的日常运维管理,经常会遇到的一种故障现象就是丢包,当网络出现传输延迟、无法连接服务器数据库等异常现象以后,如何能快速找到故障原因并排除故障．这是对网络管理人员的一大挑战,网管人员应当具备处理这种网络丢包故障的能力。列举工作中遇到的几种丢包案例,总结出故障解决的经验与心得,希望能够提高大家的分析能力和解决此类网络故障的能力。     

惯性导航装置虚拟检测仪设计

惯性导航装置是一种很重要且精密的设备,故障检测和诊断是设备维修的前提,综合采用现在的虚拟检测技术,对惯性导航装置的故障检测方法进行了研究,设计了虚拟检测仪的硬件设备,开发了虚拟检测仪的软件,完成了整个虚拟检测仪的设计。     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

基于神经网络观测器的飞控传感器在线故障诊断

为提高无人机飞行安全可靠性,针对飞行控制系统中常出现的传感器故障以及非线性气动力模型参数难以确定的问题,提出了基于BP神经网络观测器估计的故障诊断方法;引用LM改进算法对网络参数进行调整,构造了神经网络观测器模型逼近非线性系统,并运用于飞行控制系统进行在线数字仿真,对垂直陀螺输出卡死故障、恒偏差故障和恒增益故障分别进行仿真分析;仿真结果表明,所设计神经网络观测器可以有效估计系统输出,在线诊断传感器故障。     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

A self-adjusting algorithm for byzantine agreement

Summary Byzantine Agreement is important both in the theory and practice of distributed computing. However, protocols to reach Byzantine Agreement are usually expensive both in the time required as well as in the number of messages exchanged. In this paper, we present a self-adjusting approach to the problem. The Mostly Byzantine Agreement is proposed as a more restrictive agreement problem that requires that in the consecutive attempts to reach agreement, the number of disagreements (i.e., failures to reach Byzantine Agreement) is finite. Fort faulty processes, we give an algorithm that has at mostt disagreements for 4t or more processes. Another algorithm is given forn3t+1 processes with the number of disagreements belowt ²/2. Both algorithms useO(n ³) message bits for binary value agreement. Yi Zhao is currently working on his Ph.D. degree in Computer Science at University of Houston. His research interests include fault tolerance, distributed computing, parallel computation and neural networks. He obtained his M.S. from University of Houston in 1988 and B.S. from Beijing University of Aeronautics and Astronautics in 1984, both in computer science. Farokh B. Bastani received the B. Tech. degree in electrical engineering from the Indian Institute of Technology, Bombay, India, and the M.S. and Ph.D. degrees in electrical engineering and computer science from the University of California, Berkeley. He joined the University of Houston in 1980, where he is currently an Associate Professor of Computer Science. His research interests include software design and validation techniques, distributed systems, and fault-tolerant systems. He is a member of the ACM and the IEEE and is on the editorial board of theIEEE Transactions on Software Engineering.     

港口机车故障监测与诊断系统研究

针对港口机车运行现状,介绍了一种基于Visual Basic语言和Microsoft SQL Server2000数据库的机车故障监测与诊断系统的研究。该系统通过传感器采集数据信息,经预处理后,通过无线网络将整合数据传送给计算机,最后利用知识库对数据进行处理分析以及显示。该系统具有设计简单、操作方便、界面友好及可靠性好等特点。     

航空发动机传感器故障鲁棒检测方法

研究发动机传感器故障准确检测问题,现代航空发动机数字电子控制系统对传感器的可靠性要求日益提高。针对航空发动机结构复杂,又工作在高温和高压下,常规采用的传感器故障检测方法的准确性易受到建模误差与外界扰动的影响,造成漏报或误报。为了提高检测精度,提出建立航空发动机数控系统传感器未知输入故障模型,采用特征结构配置的方法,通过配置闭环系统左特征向量实现故障检测残差对不确定性因素的干扰解耦,降低扰动对故障诊断结果的影响。用某型涡扇发动机数控系统传感器故障数字仿真试验表明,所设计的方法对范数有界的不确定量可以实现干扰解耦,抑制干扰对故障检测的影响,改善检测算法的鲁棒性,提高检测结果的准确性,同时满足在线运算的实时性要求。提高了航空发动机的可靠性,保证了安全飞行。