失稳网络非均衡断网故障排除系统设计
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摘要
为解决由负载分配不均造成的断网故障问题,设计新型失稳网络的非均衡断网故障排除系统。在非均衡网络负载框架中,重新分配失稳网络故障探查模块、排除编译器两个单元结构所占比重,实现新型故障排除系统的硬件运行环境搭建。在故障排除服务器的支持下,规范断网故障数据表、非均衡失稳网络锁协议的联结形式,实现新型故障排除系统的软件运行环境搭建,结合软、硬件运行结构,完成失稳网络非均衡断网故障排除系统设计。对比实验结果显示,与现有故障排除系统相比,在非均衡网络失衡状态比为0.33和0.47的条件下,应用新型失稳网络非均衡断网故障排除系统,能使负载分配均衡率达到90%,有效解决由负载分配不均带来的断网故障现象。
A new unbalanced network outage fault removal system of unstable network is designed to eliminate the network outage fault caused by unbalanced load distribution. In the load framework of unbalanced network,the proportion of fault detection module and fault data exclusion compiler of unstable network is reallocated to build the hardware running environment of the new fault removal system. With the support of the fault removal server,the connection form of network outage fault database table and unbalanced unstable network lock protocol is standardized to build the software running environment of the new fault removal system. In combination with software and hardware running structures,the design of the unbalanced network outage fault removal system is completed. The experimental result of the proposed system is compared with that of available system,which shows that the new unbalanced network outage fault removal system of unstable network can make the load distribution balance rate up to 90% while the unbalanced state ratios of unbalanced network are 0.33 and 0.47 respectively,and eliminate the network outage fault caused by unbalanced load distribution.
A new unbalanced network outage fault removal system of unstable network is designed to eliminate the network outage fault caused by unbalanced load distribution. In the load framework of unbalanced network,the proportion of fault detection module and fault data exclusion compiler of unstable network is reallocated to build the hardware running environment of the new fault removal system. With the support of the fault removal server,the connection form of network outage fault database table and unbalanced unstable network lock protocol is standardized to build the software running environment of the new fault removal system. In combination with software and hardware running structures,the design of the unbalanced network outage fault removal system is completed. The experimental result of the proposed system is compared with that of available system,which shows that the new unbalanced network outage fault removal system of unstable network can make the load distribution balance rate up to 90% while the unbalanced state ratios of unbalanced network are 0.33 and 0.47 respectively,and eliminate the network outage fault caused by unbalanced load distribution.
引文
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[2]雷小朋,王长清.Ping命令下多媒体网络故障被动诊断方法仿真[J].计算机仿真,2017,34(8):369-372.LEI Xiaopeng,WANG Changqing. Simulation of passive fault diagnosis method for multimedia network under Ping command[J]. Computer simulation,2017,34(8):369-372.
[3]韩刚,张琛,蔡旭.电网短路故障引发的全功率风电机组频率失稳机理与控制方法[J].电工技术学报,2018,33(10):2167-2175.HAN Gang, ZHANG Chen, CAI Xu. Frequency instability mechanism and control method of full power wind turbines caused by short circuit fault in power grid[J]. Transactions of China electrotechnical society,2018,33(10):2167-2175.
[4]韩慧仙,刘建林.某型滑移式装载机工作装置液压系统振动故障分析与排除[J].机床与液压,2017,45(10):170-173.HAN Huixian,LIU Jianlin. Vibration fault analysis and elimination for hydraulic system of a certain type of skid loader[J].Machine tool&hydraulics,2017,45(10):170-173.
[5]乔文刚,赵静思,王磊.轧机活套液压控制系统状态监测与故障诊断专家系统研究[J].锻压技术,2017,42(6):122-125.QIAO Wengang,ZHAO Jingsi,WANG Lei. Research on condition monitoring and fault diagnose expert system in hydraulic control system of rolling mill looper[J]. Forging&stamping technology,2017,42(6):122-125.
[6]王明伟,陈立万,李洪兵,等.基于混合免疫系统机理的无线传感网络故障检测算法[J].仪表技术与传感器,2016,23(4):84-86.WANG Mingwei,CHEN Liwan,LI Hongbing,et al. Fault detection algorithm for wireless sensor networks based on hybrid immune system mechanism[J]. Instrument technique and sensor,2016,23(4):84-86.
[7]刘勇,沈轩帆,廖勇,等.基于遗传算法的无线传感器/执行器网络故障检测滤波器设计[J].计算机应用,2016,36(3):616-619.LIU Yong,SHEN Xuanfan,LIAO Yong,et al. Fault detection filter design based on genetic algorithm in wireless sensor and actuator network[J]. Journal of computer applications,2016,36(3):616-619.
[8]伊洋,刘育权,陈宇强,等.基于信息综合判断的智能变电站网络通信故障定位技术研究[J].电力系统保护与控制,2016,44(3):135-140.YI Yang,LIU Yuquan,CHEN Yuqiang,et al. Research of network communication fault location technique in smart substation based on comprehensive information judgment[J]. Power system protection and control,2016,44(3):135-140.
[9]陈峰,胡映月,李小红,等.城市轨道交通有权网络相继故障可靠性研究[J].交通运输系统工程与信息,2016,16(2):139-145.CHEN Feng,HU Yingyue,LI Xiaohong,et al. Cascading failures in weighted network of urban rail transit[J]. Journal of transportation systems engineering and information technology,2016,16(2):139-145.
[10]于劲松,沈琳,唐荻音,等.基于贝叶斯网络的故障诊断系统性能评价[J].北京航空航天大学学报,2016,42(1):35-40.YU Jinsong,SHEN Lin,TANG Diyin,et al. Performance evaluation of fault diagnosis system based on Bayesian network[J]. Journal of Beijing University of Aeronautics and Astronautics,2016,42(1):35-40.
[11]夏晓峰,何常胜.LSM结合邻居干扰抵抗模型的传感器网络节点故障检测[J].湘潭大学自然科学学报,2016,38(1):101-105.XIA Xiaofeng,HE Changsheng. Node fault detection for sensor network based on LSM and neighbor interference resistance model[J]. Journal of Xiangtan University(Natural Science),2016,38(1):101-105.
[12]王跃飞,张亚生,刘红军,等.基于AUTOSAR的汽车FlexRay网络通信故障检测与管理[J].农业工程学报,2016,19(4):105-112.WANG Yuefei,ZHANG Yasheng,LIU Hongjun,et al. Fault detection and management for automotive FlexRay network communication based on AUTOSAR[J]. Transactions of the Chinese society of agricultural engineering,2016,19(4):105-112.
[13]王鹤鸣,王灵矫,谭貌,等.基于多目标优化回声状态网络的电网故障恢复[J].云南大学学报(自然科学版),2017,39(5):760-767.WANG Heming,WANG Lingjiao,TAN Mao,et al. Service restoration for distributed system based on ESN and MOA[J].Journal of Yunnan University(Natural Sciences Edition),2017,39(5):760-767.
[14]孟宗,胡猛,谷伟明,等.基于LMD多尺度熵和概率神经网络的滚动轴承故障诊断方法[J].中国机械工程,2016,27(4):433-437.MENG Zong,HU Meng,GU Weiming,et al. Rolling bearing fault diagnosis method based on LMD multi-scale entropy and probabilistic neural network[J]. China mechanical engineering,2016,27(4):433-437.
[15] CHATTERJEE S,SARKAR S,HORE S,et al. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings[J]. Neural computing&applications,2017,28(8):2005-2016.