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第25届中国控制与决策会议

作者： Lv Feng Li Xiang Wang Xiu-qing College of Physics Science and Information Engineering Hebei Normal University

ISBN: (纸本)9781467355339

This paper firstly discusses the common fault types of networks, and the necessity and importance of intelligent fault diagnosis for networks. Secondly, the basic process for network diagnosis systems is put forward. Thirdly, the basic idea and various popular methods of network fault diagnosis based on the intelligent technology, such as: Expert System, Bayesian network, Rough Set and Neural networks, are reviewed. Finally, the existing problems and the future research direction for network fault diagnosis are discussed.

关键词： network fault diagnosis Pattern Recognition Machine Learning Data Mining

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A Distributed network fault diagnosis Platform based on Web Services

A Distributed Network Fault Diagnosis Platform based on Web ...

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2012 National Conference on Information Technology and Computer Science

作者： LI Bo Department of Compute Science and Engineering PLA University of Science and Technology Nanjing,China ZHANG Guo-min Department of Compute Science and Engineering PLA University of Science and Technology Nanjing,China WEI Xiang-lin Department of Compute Science and Engineering PLA University of Science and Technology Nanjing,China CHEN Ming Department of Compute Science and Engineering PLA University of Science and Technology Nanjing,China LI Bing Department of Compute Science and Engineering PLA University of Science and Technology Nanjing,China

Large-scale network services bring great challenges to fault diagnosis,which include the unpredictability of the fault,the difficulty of collecting the fault information,high cost and technical complexity of diagnosis system *** address these challenges,a web services-based distributed network fault diagnosis platform is built in this paper,which makes full use of the existing regional network management systems’ *** platform integrates passive diagnosis and active probing ***,it can obtain more network information,which provides sufficient evidence for passive fault diagnosis,and can select optimal active actions to enhance the passive diagnosis whenever *** experimental results show that our platform can efficiently and accurately localize the root causes of faults.

关键词： network fault diagnosis distributed web services platform

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The Lightweight Edge-Side fault diagnosis Approach Based on Spiking Neural network

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Computers, Materials & Continua 2024年第6期79卷 4883-4904页

作者： Jingting Mei Yang Yang Zhipeng Gao Lanlan Rui Yijing Lin State Key Laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network *** the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is ***,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN *** neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data *** by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of ***,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy ***,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN *** on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis ***,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model *** conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

关键词： network fault diagnosis edge networks Izhikevich neurons pruning dynamic spike timing dependent plasticity learning

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Process of Threshold of fault diagnosis Expert System in LAN Based on BP Neural network

Process of Threshold of Fault Diagnosis Expert System in LAN...

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第二十七届中国控制会议

作者： Yang Xiaoqing 1,Cai Tao 1,Chen Jie 1 1.Department of Automatic Control,School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,P.R.China

Traditional control system usually uses a single expert system for fault-diagnosis,which is unitary and unable to deal with problems in the complicated and flexible network *** this paper,expert system and neural network system are combined for fault detection and diagnosis,which can greatly increase the efficiency and accuracy of network fault diagnosis. The networked control system discussed in the paper have several indices for fault diagnosis,which can be processed as input of BP neural network,and the training result proved to be good.

关键词： network fault diagnosis Expert system BP neural network

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diagnosis of Intermittent Connections for DeviceNet

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Chinese Journal of Mechanical Engineering 2010年第5期23卷 606-612页

作者： LEI Yong DJURDJANOVIC Dragan The State Key Lab of FluidPower Transmission and Control Zhejiang University Hangzhou 310027 China Department of Mechanical Engineering University of Texas Austin 78712 USA

An intermittent connection is one of the major problems that affect the network reliability and communication ***,little attention has been paid to the detection,analysis and localization of the intermittent *** due to the limitations of the DeviceNet protocol,there is no effective online diagnostic tool available to identify the location of intermittent *** the basis of different DeviceNet fault scenarios induced by intermittent connections,a new graph-based diagnostic method is developed to analyze DeviceNet fault patterns,identify the corresponding fault scenarios,and infer the location of the intermittent connection problem by using passively captured network faults.A novel error source analysis tool,which integrates network data-link layer analysis and feature based network physical layer information,is developed to restore the snapshots of the network communication at each intermittent connection induced error.A graph based location identification method is developed to infer the location of the intermittent connections based on the restored error patterns.A 3-node laboratory test-bed,using master-slave polling communication method,is constructed to emulate the intermittent connection induced faults on the network drop cable by using digital switches,whose on/off states are controlled by a *** experiments,the network fault diagnosis is conducted by using information collected on trunk cable（backbone）.Experimental study shows that the proposed method is effective to restore the snapshots of the network errors and locate the drop cable that experiences the intermittent connection problem.

关键词： network fault diagnosis fieldbus DeviceNet intermittent connection

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