Construction of Network Fault Simulation Platform and Event Samples Acquisition Techniques for Event Correlation

Event correlation is one key technique in network fault management. For the event sample acquisition problem in event correlation, a novel approach is proposed to collect the samples by constructing network simulation platform. The platform designed can set kinds of network faults according to user's demand and generate a lot of network fault events, which will benefit the research on efficient event correlation techniques.

Network Fault Analysis from Passive Measurement

A new passive method for automatic discovery and location of network failure is proposed.This method employs a passive measurement to collect information and events from network traffic,and employs a model-based reasoning system to detect and locate network faults.Measurement points are deployed in a backbone network to capture the traffic and then evaluate the Quality of Service(QoS) metrics of end-to-end IP conversations.A routing model is also established for the observed network to simulate the attributes and activities of routers and links.This routing model also deduces the routing path for each IP conversation,and thus the QoS metrics of IP conversations are mapped into the metrics of paths.With the information of shared links of overlapping paths and network tomography technique,the QoS metrics of links can also be estimated,and the poorly rated links are picked out as failure points.This method is implemented in a tool named FaultMan,which is deployed in a campus network.Test results have shown its availability in middle-scale networks.

Network Fault Diagnosis Using DSM

Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules. Key words computer networks - data reduction - fault management - difference-similitude matrix CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (90204008)Biography: Jiang Hao (1976-), male, Ph. D candidate, research direction: computer network, data mine.

Overview of Distribution Network Fault Location

According to the existing research, the fault section location and fault location of passive distribution network and active distribution network are reviewed. Among them, fault location of passive distribution network mainly introduces fault segment location based on transient state and steady state quantity and fault location based on transient quantity. The active distribution network mainly introduces the fault segment location based on the current amount and the switching capacity based on the distribution network topology. On this basis, the difficulties of fault location in the distribution network at present are analyzed, and the future development is prospected.

The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering 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 necessary.Therefore,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 model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,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 consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To 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.

Waveguide Bragg Grating for Fault Localization in PON

Femtosecond laser direct inscription is a technique especially useful for prototyping purposes due to its distinctive advantages such as high fabrication accuracy,true 3D processing flexibility,and no need for mold or photomask.In this paper,we demonstrate the design and fabrication of a planar lightwave circuit(PLC)power splitter encoded with waveguide Bragg gratings(WBG)using a femtosecond laser inscription technique for passive optical network(PON)fault localization application.Both the reflected wavelengths and intervals of WBGs can be conveniently tuned.In the experiment,we succeeded in directly inscribing WBGs in 1×4 PLC splitter chips with a wavelength interval of about 4 nm and an adjustable reflectivity of up to 70% in the C-band.The proposed method is suitable for the prototyping of a PLC splitter encoded with WBG for PON fault localization applications.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

FAULT LOCATION AND RECORDING SYSTEM OF TRANSMISSION LINES BASED ON COMPUTER NETWORK

提出以计算机网络为基础的超高压输电线路的故障定位和录波系统，在结构上具有开放式的特点，可根据要求方便地接入扩建线路，并且高速网络数通讯使系统具有极好的实时性．文中重点论述了故障定位新方法，它不仅在线路故障定碍位的数学模型建立上考虑了超高压长距离线路的分布参数特性，并且在计算方法上计及故障线路两端故障电流的相位，经ＥＭＴＰ仿真及测试可证明所提出的综合方法对超高运长距离输电线路故障定位的精确性．论述了以计算机网络为基础的输电线故障定位和录波系统，给出了系统结构和主要特点，重点阐述了所提出的精确超高压长距离输电线故障定位的新方法。

Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer

Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can＇t be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015？0.017 5 and sample error is controlled within 0？0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed： complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show： intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

Fault diagnosis of time-delay complex dynamical networks using output signals

This paper proposes a novel approach for fault diagnosis of a time-delay complex dynamical network. Unlike the other methods, assuming that the dynamics of the network can be described by a linear stochastic model, or using the state variables of nodes in the network to design an adaptive observer, it only uses the output variable of the nodes to design an observer and an adaptive law of topology matrix in the observer of a complex network, leading to simple design of the observer and easy realisation of topology monitoring for the complex networks in real engineering. The proposed scheme can monitor any changes of the topology structure of a time-delay complex network. The effectiveness of this method is successfully demonstrated by virtue of a complex networks with Lorenz model.

A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

Research on Gear-box Fault Diagnosis Method Based on Adjusting-learning-rate PSO Neural Network

Based on the research of Particle Swarm Optimization (PSO) learning rate, two learning rates are changed linearly with velocity-formula evolving in order to adjust the proportion of social part and cognitional part; then the methods are applied to BP neural network training, the convergence rate is heavily accelerated and locally optional solution is avoided. According to actual data of two levels compound-box in vibration lab, signals are analyzed and their characteristic values are abstracted. By applying the trained BP neural networks to compound-box fault diagnosis, it is indicated that the methods are sound effective.

Fault Location Identification for Localized Intermittent Connection Problems on CAN Networks

The intermittent connection（IC）of the field-bus in networked manufacturing systems is a common but hard troubleshooting network problem,which may result in system level failures or safety issues.However,there is no online IC location identification method available to detect and locate the position of the problem.To tackle this problem,a novel model based online fault location identification method for localized IC problem is proposed.First,the error event patterns are identified and classified according to different node sources in each error frame.Then generalized zero inflated Poisson process（GZIP）model for each node is established by using time stamped error event sequence.Finally,the location of the IC fault is determined by testing whether the parameters of the fitted stochastic model is statistically significant or not using the confident intervals of the estimated parameters.To illustrate the proposed method,case studies are conducted on a 3-node controller area network（CAN）test-bed,in which IC induced faults are imposed on a network drop cable using computer controlled on-off switches.The experimental results show the parameters of the GZIP model for the problematic node are statistically significant（larger than 0）,and the patterns of the confident intervals of the estimated parameters are directly linked to the problematic node,which agrees with the experimental setup.The proposed online IC location identification method can successfully identify the location of the drop cable on which IC faults occurs on the CAN network.

Fault Estimation and Accommodation for a Class of Nonlinear System Based on Neural Network Observer

The problem of fault estimation and accommodation of nonlinear systems with disturbances is studied using adaptive observer and neural network techniques.A robust adaptive learning algorithm based on switchingβsmodification is developed to realize the accurate and fast estimation of unknown actuator faults or component faults.Then a fault tolerant controller is designed to restore system performance.Dynamic error convergence and system stability can be guaranteed by Lyapunov stability theory.Finally,simulation results of quadrotor helicopter attitude systems are presented to illustrate the efficiency of the proposed techniques.

Fault Tolerant Synchronization for a General Complex DynamicalNetwork with Random Delay

A fault tolerant synchronization strategy is proposed to synchronize a complex network with random time delays and sensor faults. Random time delays over the network transmission are described by using Markov chains. Based on the Lyapunov stability theory and stochastic analysis, several passive fault tolerant synchronization criteria are derived,which can be described in the form of linear matrix inequalities. Finally,a numerical simulation example is carried out and the results show the validity of the proposed fault tolerant synchronization controller.

Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system withoutput time-variant delay. White noise is considered in the model construction of NCS. By using theKalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system, a residual is generated to diagnoseme sensor faults and the actuator faults. Finally, an example is given to show the feasibility ofthe approach.

Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

Research on Evaluation of Degree of Complexity of Mining Fault Network Based on GIS

A large number of spatial and attribute data are involved in coal resource evaluation. Database is a relatively advanced data management technology, but its Major defects are the poor graphic and spatial data functions, from which it is difficult to realize scientific management of evaluation data with spatial characteristics and evaluation result maps. On account of these deficiencies, the evaluation of degree of complexity of mining fault network, based on GIS, is proposed, which integrates management of spatial and attribute data. Fractal is an index which can reflect the comprehensive information of faults' number, density, size, composition and dynamics mechanism. Fractal dimension is used as the quantitative evaluation index. Evaluation software has been developed based on a component GIS-MapX, with which the degree of complexity of fault network is evaluated quantitatively using the quantitative index of fractal dimensions in Liuqiao No.2 coal mine as an example. Results show that it is effective in acquiring model parameters and enhancing the definition of data and evaluation results with the application of GIS technology. The fault network is a system with fractal structure and its complexity can be described reasonably and accurately by fractal dimension, which provides an effective method for coal resource evaluation.

Fault detection observer design for networked control system with long time-delays and data packet dropout

Focusing on the networked control system with long time-delays and data packet dropout,the problem of observerbased fault detection of the system is studied.According to conditions of data arrival of the controller,the state observers of the system are designed to detect faults when they occur in the system.When the system is normal,the observers system is modeled as an uncertain switched system.Based on the model,stability condition of the whole system is given.When conditions are satisfied,the system is asymptotically stable.When a fault occurs,the observers residual can change rapidly to detect the fault.A numerical example shows the effectiveness of the proposed method.

Research on fault-tolerant control of networked control systems based on information scheduling

A kind of networked control system is studied; the networked control system with noise disturbance is modeled based on information scheduling and control co-design. Augmented state matrix analysis method is introduced, and robust fault-tolerant control problem of networked control systems with noise disturbance under actuator failures is studied. The parametric expression of the controller under actuator failures is given. Furthermore, the result is analyzed by simulation tests, which not only satisfies the networked control systems stability, but also decreases the data information number in network channel and makes full use of the network resources.