Graph Attention Residual Network Based Routing and Fault-Tolerant Scheduling Mechanism for Data Flow in Power Communication Network

For permanent faults(PF)in the power communication network(PCN),such as link interruptions,the timesensitive networking(TSN)relied on by PCN,typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability,which often limits TSN scheduling performance in fault-free ideal states.So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism(GRFS)for data flow in PCN,which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding(CQF)model and fault recovery method,which reduces the impact of faults by simplified scheduling configurations of CQF and fault-tolerance of prioritizing the rerouting of faulty time-sensitive(TS)flows;considering that PF leading to changes in network topology is more appropriately solved by doing routing and time slot injection decisions hop-by-hop,and that reasonable network load can reduce the damage caused by PF and reserve resources for the rerouting of faulty TS flows,an optimization model for joint routing and scheduling is constructed with scheduling success rate as the objective,and with traffic latency and network load as constraints;to catch changes in TSN topology and traffic load,a D3QN algorithm based on a multi-head graph attention residual network(MGAR)is designed to solve the problem model,where the MGAR based encoder reconstructs the TSN status into feature embedding vectors,and a dueling network decoder performs decoding tasks on the reconstructed feature embedding vectors.Simulation results show that GRFS outperforms heuristic fault-tolerance algorithms and other benchmark schemes by approximately 10%in routing and scheduling success rate in ideal states and 5%in rerouting and rescheduling success rate in fault states.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

CF-BFT:A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication

The consensus protocol is one of the core technologies in blockchain,which plays a crucial role in ensuring the block generation rate,consistency,and safety of the blockchain system.Blockchain systems mainly adopt the Byzantine Fault Tolerance(BFT)protocol,which often suffers fromslow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus.In this paper,this paper proposes a new dual-mode consensus protocol based on node identity authentication.It divides the consensus process into two subprotocols:Check_BFT and Fast_BFT.In Check_BFT,the replicas authenticate the primary’s identity by monitoring its behaviors.First,assume that the systemis in a pessimistic environment,Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node;Enter the fast consensus stage after confirming the environmental safety,and implement Fast_BFT protocol.It is assumed that there are 3f+1 nodes in total.If more than 2f+1 nodes identify that the primary is honest,it will enter the Fast_BFT process.In Fast_BFT,the primary is allowed to handle transactions alone,and the replicas can only receive the messages sent by the primary.The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol.Compared with the SAZyzz protocol,the throughput is increased by 3 times in the best case and 60%in the worst case.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems,digital filters are widely employed.In some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter implementations.Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters.As technology advances,more complicated systems with several filters become possible.Some of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input signals.Recently,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given.Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years.The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules.Finally,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising.This approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR modules.From the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation.Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

FAULT-TOLERANT INTEGRATED NAVIGATION SYSTEM BASED ON NEURONS

In this paper, the multisensor data fusion technique of a fault tolerant integrated navigation system is discussed. A neural approach for data fusion is proposed for multisensor integrated systems. The simulation results show that this neural approach for data fusion is feasible.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems

This article studies the fault detection filtering design problem for Roesser type two-dimensional(2-D)nonlinear systems described by uncertain 2-D Takagi-Sugeno(T-S)fuzzy models.Firstly,fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited,based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to faults.It has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional nonfrequency based filtering design approach.Then,with the proposed evaluation function and its threshold,a novel mixed finite frequency H_(∞)/H_(-)fault detection algorithm is developed,based on which the fault can be immediately detected once the evaluation function exceeds the threshold.Finally,it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

Model-Based Fault Tolerant Control for Hybrid Dynamic Systems with Sensor Faults

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

On-Line Fault Diagnosis and Fault-Tolerant Operation of Modular Multilevel Converters –A Comprehensive Review

Modular multilevel converters(MMCs)have been one of the most broadly used multilevel converter topologies in industrial applications,particularly in medium-voltage motor drives and high-voltage dc power conversion systems.However,due to the utilization of large amount of semiconductor devices,the reliability of MMCs becomes one of the severe challenges constraining their further development and applications.In this paper,common electrical faults of the MMC have been summarized and analyzed,including open-circuit switching faults,short-circuit switching faults,dc-bus short-circuit faults,and single line-to-ground faults on the ac side.A thorough and comprehensive review of the existing online fault diagnostic methods has been conducted.In addition,fault-tolerant operation strategies for such various fault scenarios in MMCs have been presented.All the fault diagnosis and fault-tolerant operation strategies are comparatively evaluated,which aims to provide a state-of-the-art reference on the MMC reliability for future research and industrial applications.

Open-circuit Fault-tolerant Control Strategy Based on Five-level Power Converter for SRM System

Switched reluctance motor power converters are prone to open-circuit faults because it need to withstand large voltages and currents.Due to the small number of traditional asymmetrical half bridge topology switches,it is difficult to carry out fault tolerant control when power converters has an open-circuit fault,resulting in larger output torque ripple.This paper presents a five-level power converter based on the traditional asymmetric half-bridge power converter.The five-level topology has more switching states and can work in multi-level mode.Based on the topology,different excitation and demagnetization voltages can be choose at different speeds.A fault-tolerance strategy is developed to decrease the influence of the open-circuit fault.The five-level power converter has four switches per phase,and two of them will be used in one of the operating mode.So the remaining two of the switches can be used for safe backup,enabling fault-tolerant control when an open-circuit occur.Since each phase of the five-level power converter proposed in this paper is independent of each other,a reasonable control strategy can be used to avoid the unbalance of the midpoint potential.Finally,the topology and fault-tolerant strategy proposed in this paper are verified by simulation and experiment.

Event-triggered sampling and fault-tolerant control co-design based on fault diagnosis observer

A co-design scheme of event-triggered sampling mechanism and active fault tolerant control(FTC) is developed. Firstly,a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H∞constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost.Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.

VERTEX-FAULT-TOLERANT CYCLES EMBEDDING ON ENHANCED HYPERCUBE NETWORKS

In this paper, we study the enhanced hypercube, an attractive variant of the hypercube and obtained by adding some complementary edges from a hypercube, and focus on cycles embedding on the enhanced hypercube with faulty vertices. Let Fu be the set of faulty vertices in the n-dimensional enhanced hypercube Qn,k （n ≥ 3, 1 ≤ k 〈≤n - 1）. When IFvl = 2, we showed that Qn,k - Fv contains a fault-free cycle of every even length from 4 to 2n - 4 where n （n ≥ 3） and k have the same parity; and contains a fault-free cycle of every even length from 4 to 2n - 4, simultaneously, contains a cycle of every odd length from n-k ＋ 2 to 2^n-3 where n （≥ 3） and k have the different parity. Furthermore, when ｜Fv｜ = fv ≤ n - 2, we prove that there exists the longest fault-free cycle, which is of even length 2^n - 2fv whether n （n ≥ 3） and k have the same parity or not; and there exists the longest fault-free cycle, which is of odd length 2^n - 2fv ＋ 1 in Qn,k - Fv where n （≥ 3） and k have the different parity.

Fault-tolerant Control Systems-An Introductory Overview

This paper presents an introductory overview on the development of fault-tolerant control systems. For this reason, the paper is written in a tutorial fashion to summarize some of the important results in this subject area deliberately without going into details in any of them. However, key references are provided from which interested readers can obtain more detailed information on a particular subject. It is necessary to mention that, throughout this paper, no efforts were made to provide an exhaustive coverage on the subject matter. In fact, it is far from it. The paper merely represents the view and experience of its author. It can very well be that some important issues or topics were left out unintentionally. If that is the case, the author sincerely apologizes in advance.After a brief account of fault-tolerant control systems, particularly on the original motivations, and the concept of redundancies, the paper reviews the development of fault-tolerant control systems with highlights to several important issues from a historical perspective. The general approaches to fault-tolerant control has been divided into passive, active, and hybrid approaches. The analysis techniques for active fault-tolerant control systems are also discussed. Practical applications of faulttolerant control are highlighted from a practical and industrial perspective. Finally, some critical issues in this area are discussed as open problems for future research/development in this emerging field.

Sensor fault-tolerant observer applied in UAV anti-skid braking control under control input constraint

This paper proposes a method for addressing the problem of sensor fault-tolerant control (FTC) for anti-skid braking systems (ABSs). When the wheel velocity sensor of the ABS for unmanned aerial vehicles (UAVs) becomes faulty, wheel velocity failure and feedback instability may occur. Firstly, a fault diagnosis and isolation (FDI) method based on a sliding mode observer approach is introduced to detect and isolate the fault of the sensor. When the wheel velocity sensor is in healthy conditions, the observer works in a diagnosis mode. If faults occur in the sensor, it acts as a wheel velocity estimator. Secondly, an FTC strategy, adopting a feedback compensation structure, is designed with input control constraints. In addition, based on the FDI result, a terminal sliding mode (TSM) controller is designed to guarantee that slip-ratio tracks its appropriate reference values in situations where runways change conditions during landing. The control system switches automatically from control using a wheel velocity sensor to sensorless control mode, so the observer-based FTC scheme is established. It is logical that the ABS keeps observed-state and remains stable when the wheel velocity sensor is broken and during external disturbance. Finally, simulation results show the effectiveness of the proposed method. © 1990-2011 Beijing Institute of Aerospace Information.

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi Sugeno （T-S） fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov-Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.