Fault-tolerant Capability Research Articles

Analysis of fault tolerance capability of QCA circuit under regular clocking

ABSTRACT With significant progress in device scaling, current CMOS technology faces challenges such as low device density and increased power dissipation. Quantum-dot Cellular Automata (QCA) stands as one of the alternative nanotechnologies that proved viable to overcome these hurdles. The fundamental building block for implementing logic circuits is a QCA cell, which includes two vertically positioned electrons within four quantum dots. The majority gate and inverters are considered to be the fundamental primitives in QCA. On the other hand, clocking serves a crucial part in ensuring the correct synchronisation and information transmission within a circuit. Furthermore, regular clocking resolves fabrication pitfalls at the nanoscale and supports scalability. However, defects remain a concern in nanoscale circuit realisation. This research investigates the performance of logic circuits realised in regular clocking concerning the potentiality to tolerate faults. The circuits in both combinational and sequential logic are examined. For this purpose, the HDLQ and QCADesigner simulators are utilised. In accordance with the investigation, the performance of the circuits realised using zig–zag clocking is noteworthy, whereas the USE clock is noticed in a few cases.

Technological Trends for Electrical Machines and Drives Used in Small Wind Power Plants—A Review

High-power-range wind generators mainly employ classical variants, with the advantages of low cost, high robustness and acceptable energetic performance, while for low-power applications, the available electrical drive solutions are more numerous. This paper investigates the current trend in this field, indicating simple or complex structures, with or without self-excitation and with or without mechanical or magnetic transmission. The discussed variants are compared in terms of complexity, cost, fault-tolerance capability and estimated energetic performances but also the grid connectivity for standard conditions. The review is completed by testing options and conditions, as well as the methods for parameter determination, which have an important effect on the controllability of the entire system.

Analysis of a Novel Dual Winding Dual Magnet Machine With Compound Fault-Tolerance Capability

Analysis of a Novel Dual Winding Dual Magnet Machine With Compound Fault-Tolerance Capability

HARMONIC ANALYSIS OF MAGNETOMOTIVE FORCE IN THE AIR GAP OF SIX-PHASE ASYNCHRONOUS MACHINES

Asynchronous motors with six and more phases (usually n-three-phase) are increasingly accepted in electric drive systems of road transport, marine and even aeronautical transport. Multiphase machines are credited with multiple advantages such as: less torque ripple, better power distribution per phase, higher efficiency and fault tolerance capability compared to traditional three-phase machines. There are many publications, but few of them are dedicated to the in-depth study of the multiphase motor (m>3) and especially of the magnetic field in its air gap. The purpose of the present paper: the development of the mathematical model of the magnetomotive force in the air gap of the six-phase asynchronous machines, which determines the fundamental properties of the machine, the structure of the magnetic field and the torque developed, the efficiency, the error tolerance. The research is based on analytical studies and the results of suitable experimental measurements. The paper presents the mathematical model for the harmonic analysis of the magnetomotive force in the air gap of six-phase machines with possible variants of windings on the stator. Based on this study, it was demonstrated that only n-three-phase machines with asymmetrically arranged stator windings have the ability to attenuate higher harmonics with a major negative effect on the developed torque, the applicability and efficiency of traditional tools for reducing higher harmonics in the case of six-phase machines was confirmed. The results of measurements carried out on samples of six-phase machines confirm the findings and conclusions formulated in the paper.

Two decades of statistical approach in reliability prediction of power converters

Various statistical methods are employed to predict the reliability of electronic components and monitor improvements in reliability. This article conducts a comprehensive study of statistical approaches for predicting the reliability of power converters, as reported in the literature. The analysis focuses on three statistical methods: MIL-HDBK-217F, FIDES, and RIAC. Among these, the military handbook MIL-HDBK-217F has been a dominant tool for failure analysis over the past two decades. The general formulas used for failure rate analysis of power converter parts with each of the three approaches are presented. The study also reviews and discusses failure rate analysis for components and modules in power converters, both with and without fault-tolerant capabilities, as documented in the literature using MIL-HDBK-217F, FIDES, and other statistical approaches. Finally, the paper outlines the conclusions drawn from the study and highlights the main limitations of the three methods. This article serves as a resource for researchers working on fault analysis in power converters.

Low capacitor stress reconfigurable quadratic boost converter with fault tolerant capability for rooftop solar PV application.

Recently, many high-gain topologies have been derived. However, there is a need for a high-gain converter with fault-tolerant features. In this paper, a fault-tolerant reconfigurable quadratic boost converter is proposed for DC microgrid application. In this novel topology, 2-level redundancy is achieved by addressing the fault in the switch and capacitors. The operation of the converter in normal operating conditions and reconfiguration mode is discussed. The derived topology can achieve the same voltage gain even in the reconfiguration mode. The proposed topology exhibits better performance in the reconfiguration state. The voltage stress across the input and output capacitor is reduced in that state. The reliability analysis of capacitors in both states is carried out and compared with the aid of the reliability handbook. Finally, the topology operation in a normal and reconfiguration state is validated by building a 1-kW hardware setup. The results show that the quadratic boost converter in a reconfiguration state operates without altering the voltage gain of the converter and with reduced voltage stress across the capacitors.

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.

Enabling high reliability via matroidal connectivity and conditional matroidal connectivity on arrangement graph networks

Connectivity indicators are commonly used to evaluate system fault tolerance and reliability. However, with the high demand for multi-processor systems in high-performance computing and data center networks, the number of processors is getting larger, and the network is getting more complex. Thus, traditional connectivity and other indicators are hardly competent in assessing the reliability of complex networks. The matroidal connectivity and conditional matroidal connectivity are novel connectivity metrics that measure the actual fault-tolerant capability based on the constraints of each network dimension. In this paper, we study matroidal connectivity and conditional matroidal connectivity of the (n,k)-arrangement graph network An,k from the natural perspective of the partition of edge dimension and obtain their theoretically accurate values. Moreover, we conduct numerical analysis to compare matroidal connectivity with other conditional edge connectivities in An,k. Additionally, we explore the distribution pattern of edge failure through simulation experiments in An,k and attain the relation of conditional matroidal connectivity related to network scales. Our investigations include two famous network classes: alternating group graphs and star graphs.

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.

Online DMD-based Kalman filter for current sensor fault-tolerant control of MMC-HVDC transmission systems

With the growing application of modular multilevel converter based high-voltage direct current transmission (MMC-HVDC), the sensor fault-tolerant capability in modular multilevel converters has become critical. This paper aims to boost the sensor fault-tolerant performance of the MMC-HVDC systems using advanced data-driven techniques. A novel online dynamic mode decomposition based Kalman filter (ODMDKF) is designed for fault detection and control reconfiguration. Besides, a modified dual-check fault detection module based on local outlier factor algorithm is proposed to reduce false alarm rate. Simulation studies are conducted on a modified IEEE 9-bus system with MMC-HVDC transmission lines. The results indicate that the proposed sensor fault-tolerant control method can ensure the continuous operation of the MMC-HVDC systems under the condition of three types of current sensor faults and its fault-tolerant capability is better than that of extended Kalman filter-based fault-tolerant control method.

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.