Fault-tolerant Capability Research Articles

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model’s capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.

Evaluative assessment of a five-phase and three-phase permanent magnet synchronous machine at varied loads and fault conditions

Multiphase machines are very essential for industrial applications that require high reliability of operation. In this paper, a comparative study of three-phase and five-phase inverter fed permanent magnet synchronous motors (PMSMs) was evaluated with respect to their performance characteristics under a healthy state and during transient disturbances such as varied load and double line to ground faults. To avert inherent high oscillations in speed and torque ripples during fault condition, the machine load was significantly reduced to 1/4th of the full load and a post fault current limiting series dynamic braking resistance (SDBR) with a bypassed switch was introduced to enhance the machine fault tolerant level and improve its operational performance. Spectral analyses were carried out to determine the magnitude of harmonic distortion during these conditions. The simulation results show that the five-phase PMSM achieved a reduced oscillatory transient and a faster settling time of speed and torque under a varied load. It also exhibited good harmonic profiles as indicated in the respective % THD values when compared to the three phase PMSM which is prone to high harmonic overheat. However, when subjected to a double line to ground fault, their various %THD values in speed and torque increased with five-phase PMSM still having a reduced %THD values over the three-phase PMSM. Therefore, for a higher fault tolerant capability, greater efficiency with proven reliability, a five-phase PMSM with a fault current limiting series dynamic braking resistance is a better substitute when compared with the three-phase PMSM in industrial applications where safety and loss minimization is a top priority. All simulation processes in this paper were achieved in MATLAB 2015.

A systematic review of fault tolerance techniques for smart city applications

Smart City Applications encompass many characteristics that increase the risk of failures, such as context-awareness, adaptiveness, distribution and heterogeneity. Therefore, it is important to implement fault-tolerant mechanisms to produce more reliable applications. This study presents a systematic literature review of fault tolerance techniques that have been proposed for, or applied to Smart City Applications. It also characterizes faults, errors and failures that may occur in these systems. To the best of our knowledge, this is the first review that provides a broad picture of the research area and points out research limitations and directions. We selected 43 primary studies and performed initial classifications (e.g., based on type of research, type of contribution, application domains and subdomains, and type of system architecture). We further classified and discussed the selected studies based on types of fault tolerance techniques and types of faults and failures. System Reconfiguration, Diversity, and Retry are classical techniques that have been investigated in this domain. Many fault and failure types have also been addressed. While those well-known techniques have been explored for introducing fault tolerance capabilities into Smart City Applications, others have been overlooked. Moreover, evidence on the effectiveness and applicability of the proposed fault tolerance solutions is still very limited.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Unified Fault-Tolerant Control and Adaptive Velocity Planning for 4WID-4WIS Vehicles under Multi-Fault Scenarios

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.

A new structural reconfiguration for multilevel inverters with fault tolerance capability

Keeping electrical systems running is critical in industries especially in those that rely on static converters, for example in the case of inverters, continuous and safe of operation must be ensured even if one of the inverter’s static switch modules fails. This paper presents a method for fault detection in static converters. The study presents a 3-phase 3-level NPC (Neutral Point Clamped) inverter with IGBTs as static switches where open-circuit or closed-circuit faults are considered. In addition, leg failure and structural reconfiguration are also considered in this fault-tolerant inverter by adding a fourth 3-level leg.

Generalized [formula omitted]-sparse algorithms for constructing fault tolerant RBF networks

In the construction process of radial basis function (RBF) networks, two common crucial issues arise: the selection of RBF centers and the effective utilization of the given source without encountering the overfitting problem. Another important issue is the fault tolerant capability. That is, when noise or faults exist in a trained network, it is crucial that the network’s performance does not undergo significant deterioration or decrease. However, without employing a fault tolerant procedure, a trained RBF network may exhibit significantly poor performance. Unfortunately, most existing algorithms are unable to simultaneously address all of the aforementioned issues. This paper proposes fault tolerant training algorithms that can simultaneously select RBF nodes and train RBF output weights. Additionally, our algorithms can directly control the number of RBF nodes in an explicit manner, eliminating the need for a time-consuming procedure to tune the regularization parameter and achieve the target RBF network size. Based on simulation results, our algorithms demonstrate improved test set performance when more RBF nodes are used, effectively utilizing the given source without encountering the overfitting problem. This paper first defines a fault tolerant objective function, which includes a term to suppress the effects of weight faults and weight noise. This term also prevents the issue of overfitting, resulting in better test set performance when more RBF nodes are utilized. With the defined objective function, the training process is designed to solve a generalized M-sparse problem by incorporating an ℓ0-norm constraint. The ℓ0-norm constraint allows us to directly and explicitly control the number of RBF nodes. To address the generalized M-sparse problem, we introduce the noise-resistant iterative hard thresholding (NR-IHT) algorithm. The convergence properties of the NR-IHT algorithm are subsequently discussed theoretically. To further enhance performance, we incorporate the momentum concept into the NR-IHT algorithm, referring to the modified version as “NR-IHT-Mom”. Simulation results show that both the NR-IHT algorithm and the NR-IHT-Mom algorithm outperform several state-of-the-art comparison algorithms.

A learning-based nearly optimal control framework for trajectory tracking of a flexible-link manipulator system with actuator fault

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework.

Two-Input Single-Output Boost Converter with Fault Tolerant Operation

The need for multi-input DC-DC converters is highly demanded in the context of the integration of different energy sources. When integrating several energy sources, such as batteries, solar PV arrays, fuel cells, etc., multi-input DC-DC converters preferred over multiple single-input DC-DC converters. The vast majority of MISO converters in use today only have one mode of operation, which is conventional operation using all sources. Very few MISO topologies have been presented with fault-tolerant capability. In this work, a non-isolated two-input single-output (MISO) boost converter is investigated for low-voltage DC (LVDC) applications with fault tolerant capability. The presented converter works in three modes, such as DC sources of equal values, DC sources of unequal values, and one of the DC sources that is faulty or out of order. The converter is simulated in a MATLAB/Simulink environment and experimentally validated using a scaled prototype. The outcomes demonstrate that the converter can integrate systems with two DC energy sources, accommodates DC sources of equal values, DC sources of unequal values, and works even when one of the DC sources is faulty or out of order. This work points out that the presented MISO converter would be an apt solution for integrating varying input voltage sources with fault-tolerant capability.

Fault-Tolerant Control of Floating Wind Turbine With Switched Adaptive Sliding Mode Controller

The fast-growing development of floating wind turbines demands control systems capable of both reducing output power fluctuations and fault-tolerant function. In this paper, we propose an adaptive switched sliding mode controller to enhance the performance of floating wind turbine systems in the presence of environmental uncertainties and actuator faults. A control-oriented switched linear model for floating wind turbines is introduced, considering the average dwell time. Based on the proposed controller, a full-order state observer and an adaptive law compensate for the debilitated control outputs resulting from detecting errors, disturbances, and faults. The control parameters are derived by solving stability theorems, which are proved by the Lyapunov stability theory, linear matrix inequality technique, and average dwell time technique. The proposed model and controller are validated on the NREL 5MW wind turbine and spar-buoy platform using the high-fidelity fatigue, aerodynamics, structures, and turbulence (FAST) code. The performance of the proposed controller is compared with an optimal gain-scheduling proportional-integral controller under different wind-wave combined conditions. The results show that the proposed controller improves the power quality and attenuates mechanical loads of floating wind turbine under healthy and faulty conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Floating wind turbines have drawn significant interest in renewable energy. When operating in the ocean far from shore, it is of great importance for floating wind turbines to have fault-tolerance capabilities for achieving stable power quality when faults occur in one or more components. A challenging problem is how to mitigate the fault effects on the wind turbine system. This paper proposes an adaptive fault-tolerant control strategy in the case of actuator fault occurrence in floating wind turbines.

Electric field induced mechanical flapping motors enabling soft robotic and wearable applications

Here we present a new type of soft motors, termed Electric Field Induced Mechanical Flapping (EFIMF) motors, which are robust and present versatile potential applications. The EFIMF motors harness an actuation mechanism that combines two processes: 1) using the electric field to induce charges, and 2) coupling the electromechanical forces to achieve fast and robust actuation. Our EFIMF motor can remain functional even with its electrode punctured by a metal needle. The EFIMF motors also present the fault-tolerant capability, which helps them to survive the dielectric breakdown. We demonstrate a robust crawling soft robot, which is powered by one EFIMF motor and capable of surviving continuous hammering. We also show a wearable haptic glove powered by five EFIMF motors, generating rich notification signals with a wide frequency range and different mechanical feedback intensities. The robustness of the soft EFIMF motors enhances their lifetime and performances, and represents an important step towards realistic soft mobile machines and wearable human machine interfaces.

The prediction of sound absorption coefficient of film multi-cavity materials based on generalized regression neural network (GRNN)

In recent years, noise problems have attracted more and more widespread attention with the rapid development of economy, social life and transportation construction. Sound can cause the membrane to vibrate, thereby absorbing sound waves of specific frequencies. In this study, the film was transformed into a bubble structure to form a film multi-cavity structure material. The theoretical calculation model of the sound absorption performance of sound-absorbing materials has always been the focus of research. Generalized regression neural network (GRNN) is a radial basis neural network (RBF) with a flexible network structure, high fault tolerance and strong nonlinear mapping capabilities. This paper uses the generalized regression neural network (GRNN) method to predict the sound absorption coefficient of film multi-cavity materials. Twenty-four sets of film multi-cavity material samples are prepared, and two groups of GRNN model is established. Each group of GRNN model uses 12 sets of data, of which 10 sets are used as training samples for GRNN and 2 sets are used as test samples. The thickness, bubble diameter and porosity of the material are used as inputs of the GRNN, and the sound absorption coefficients at 1/3 octave center frequencies are used as the output of the network. The optimal spread factors obtained by training the two groups of GRNN models are 0.5 and 0.35 respectively. The results show that the minimum error between the model prediction value and the experimental measurement value is 0.001. The established GRNN-1 and GRNN-2 models have high accuracy and high similarity in sound absorption curves. This method is simple, effective and accurate. When there are fewer data samples, GRNN also has outstanding performance in terms of approximation ability and learning speed.

Analysis of Closed Loop Current Controlled BLDC Motor Drive

Brushless direct current (BLDC) motors are mostly preferred for dynamic applications such as automotive industries, pumping industries, and rolling industries. It is predicted that by 2030, BLDC motors will become mainstream of power transmission in industries replacing traditional induction motors. Though the BLDC motors are gaining interest in industrial and commercial applications, the future of BLDC motors faces indispensable concerns and open research challenges. Considering the case of reliability and durability, the BLDC motor fails to yield improved fault tolerance capability, reduced electromagnetic interference, reduced acoustic noise, reduced flux ripple, and reduced torque ripple. To address these issues, closed-loop vector control is a promising methodology for BLDC motors. In the literature survey of the past five years, limited surveys were conducted on BLDC motor controllers and designing. Moreover, vital problems such as comparison between existing vector control schemes, fault tolerance control improvement, reduction in electromagnetic interference in BLDC motor controller, and other issues are not addressed. This encourages the author in conducting this survey of addressing the critical challenges of BLDC motors. Furthermore, comprehensive study on various advanced controls of BLDC motors such as fault tolerance control, Electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and its principle of operation in reducing torque ripples are discussed in detail. This paper also discusses BLDC motor history, types of BLDC motor, BLDC motor structure, Mathematical modeling of BLDC and BLDC motor standards for various applications. Key Words: Brushless direct current, industries, direct torque control.

Design and Analysis of Five-Phase Fault-Tolerant Vernier Permanent Magnet Machines for Direct-Drive Occasions

Abstract A fault-tolerant vernier permanent magnet machine (FTVPMM) with five phases for direct-drive occasions is designed and analyzed. The construction of the proposed FTVPMM is introduced and the principle of the vernier modulation effect is investigated. Parameters are designed and optimized by finite element analysis (FEA), including tooth width, slot opening, PM thickness, pole-arc coefficient, air-gap length, and rotor yoke. Furthermore, major electromagnetic performances are discussed, including cogging torque, no-load back electromotive force (BEMF), output torque, and fault-tolerant capability.

Development and Analysis of Six-Phase Synchronous Reluctance Motor for Increased Fault Tolerance Capabilities

This paper contains research on the development of a fault-tolerant six-phase synchronous reluctance motor (SynRM) based on the stator adopted from a general-purpose three-phase induction motor. In the design and calculation process, an extended Clarke transformation was developed for a six-phase asymmetrical system. To verify the proposed design approach, a field–circuit model of electromagnetic phenomena in the studied motor was developed and used to study the motor performance. The increased torque value and reduction in torque ripples were confirmed by comparison to the classical three-phase SynRM design. To illustrate fault tolerance capabilities, the operation of the studied three- and six-phase synchronous reluctance motors under inverter-fault conditions was examined. The conducted analysis shows, among other things, that from the electromagnetic performance point of view, only the proposed six-phase machine is able to properly operate under inverter-fault conditions. The results of the winding design calculations, the performed simulations of six-phase motor operation, and the preliminary tests of the prototype motor are presented and discussed.

Event‐triggered obstacle avoidance control for autonomous surface vehicles with actuator faults

AbstractThe article addresses the event‐triggered obstacle avoidance control problem for autonomous surface vehicles subject to actuator faults. In order to tackle the challenges presented by unknown actuator faults, internal model uncertainties, and external disturbances, we propose a fault‐tolerant control scheme that relies on an extended state observer to estimate the unknown parameters. In addition, considering the occurrence of actuator faults, a novel event‐triggered mechanism is developed to reduce wear of actuator and ensures the performance of system. On this basis, we employ backstepping technique and an improved artificial potential function methods to develop an event‐triggered obstacle avoidance control scheme with the capability of fault tolerance. This proposed control strategy ensures the uniform ultimate boundedness of tracking errors. In contrast to existing results, the presented control strategy simultaneously holds the performance of obstacle avoidance and fault tolerance and reduce the update frequency of actuators. The effectiveness of the provided control scheme has been confirmed through simulation.

Modeling the performance of early fault-tolerant quantum algorithms

Progress in fault-tolerant quantum computation (FTQC) has driven the pursuit of practical applications with (EFTQC). These devices, limited in their qubit counts and fault-tolerance capabilities, require algorithms that can accommodate some degrees of error, which are known as EFTQC algorithms. To predict the onset of early quantum advantage, a comprehensive methodology is needed to develop and analyze EFTQC algorithms, drawing insights from both the methodologies of noisy intermediate-scale quantum and traditional FTQC. To address this need, we propose such a methodology for modeling algorithm performance on EFTQC devices under varying degrees of error. As a case study, we apply our methodology to analyze the performance of randomized Fourier estimation (RFE) [Kshirsagar, Katabarwa, and Johnson, ], an EFTQC algorithm for phase estimation. We investigate the runtime performance and the fault-tolerant overhead of RFE in comparison to the traditional quantum phase estimation algorithm. Our analysis reveals that RFE achieves significant savings in physical qubit counts while having a much higher runtime upper bound. We anticipate even greater physical qubit savings when considering more realistic assumptions about the performance of EFTQC devices. By providing insights into the performance trade-offs and resource requirements of EFTQC algorithms, our work contributes to the development of practical and efficient quantum computing solutions on the path to quantum advantage. Published by the American Physical Society 2024

Optimal design and analysis of a dual-3phase permanent magnet motor with fault tolerant capability

Optimal design and analysis of a dual-3phase permanent magnet motor with fault tolerant capability

Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters

The Modular multilevel matrix converter (M3C) is a direct ac-ac power converter that presents several features such as scalable and flexible structure, high efficiency, fault-tolerant capability, and high power quality. However, this converter requires a complex control strategy to govern the cluster currents and balance the internal floating capacitor voltages among clusters and cells. To fulfill these requirements, this work proposes a long-horizon sequential finite-control-set model predictive control (sFCS-MPC). The resulting predictive strategy is formulated to perform both the local cell balancing control and to regulate cluster currents of the M3C in a unified control algorithm, considering the nonlinear and coupled dynamic model of each cluster. Additionally, the total available combinations and the average cell switching frequency are reduced. The proposed sFCS-MPC is implanted for a prediction horizon from one to four, and compared with some previous FCS-MPC approaches. Experimental results on a 3 kW prototype are provided to demonstrate the effectiveness and high-quality performance of the proposed strategy.

Distributed predefined-time estimator-based affine formation target-enclosing maneuver control for cooperative underactuated quadrotor UAVs with fault-tolerant capabilities

The paper presents a two-layer, disturbance-resistant, and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles (QUAVs). This scheme mainly consists of predefined-time estimators and fixed-time tracking controllers, with a hybrid Laplacian matrix describing the communication among these QUAVs. At the first layer, we devise predefined time estimators for leading and following QUAVs, enabling accurate estimation of desired information. In the second layer, we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults. Fixed-time translational tracking controllers are then proposed, and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers. We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly. The paper includes a demonstration of the control system stability through mathematical proof, as well as the presentation of simulation results and comparative simulations.

Byzantine Fault-Tolerant Federated Learning Based on Trustworthy Data and Historical Information

Federated learning (FL) is a highly promising collaborative machine learning method that preserves privacy by enabling model training on client nodes (e.g., mobile phones, Internet-of-Things devices) without sharing raw data. However, FL is vulnerable to Byzantine nodes, which can disrupt model performance, render training ineffective, or even manipulate the model by transmitting harmful gradients. In this paper, we propose a Byzantine fault-tolerant FL algorithm called federated learning with trustworthy data and historical information (FLTH). It utilizes a small trusted training dataset at the parameter server to filter out gradient updates from suspicious client nodes during model training, which provides both Byzantine resilience and convergence guarantee. It further introduces a historical information-based credibility assessment scheme such that the client nodes performing poorly over the long-term have a lower impact on the aggregation of gradients, thereby enhancing fault tolerance capability. Additionally, FLTH does not compromise the training efficiency of FL because of its low time complexity. Extensive simulation results show that FLTH achieves higher model accuracy compared to state-of-the-art methods under typical kinds of attack.