Transient Faults Research Articles

LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multi-Core Systems

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this paper presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this paper, the number of tolerable faults for each execution section of a task, and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the QoS of low-criticality tasks, and managing energy consumption with an average of 29.49%, and 32.78%, respectively.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Unknown fault detection

Multiple advanced reactor designs envision deployment scenarios that feature reactor operations with significantly reduced operating staff compared to present reactors, or even feature fully autonomous operations. Since many of these new reactor designs, like small modular reactors (SMRs) and microreactors, also include extended fuel cycles which limit inspection intervals, load-following capabilities, and reduced access to critical components, safe and reliable semi- or fully autonomous operations must be enabled through an on-line monitoring (OLM) system that effectively detects and diagnoses any faults in the reactor. This paper presents the development of the Fault Detection Module of the previously proposed intelligent OLM system: the Fault Detection and Diagnosis Monitoring System (FDDMS). The Fault Detection Module monitors various plant parameters from 86 sensors throughout the nuclear power plant and accurately defines operations as nominal or abnormal. The methodology leverages the advantages of unsupervised learning and semi-supervised optimization data-driven techniques to successfully detect any fault in the system in real-time without fault information a priori. This allows the Fault Detection Module to identify any unknown fault that may affect the system. When integrated with the rest of the FDDMS architecture, the Fault Detection Module provides power transient dependent fault detection by using a separate model for steady state, ramping up in power, and ramping down in power operations. This makes the FDDMS especially applicable for load-following reactor systems. The Fault Detection Module architecture features a dimensionality reduction algorithm followed by an anomaly detection method. Three dimensionality reduction techniques: 1) Principal Component Analysis (PCA), 2) Deep Neural Network Autoencoder (DNNAE), and 3) Convolutional Neural Network Autoencoder (CNNAE); and three anomaly detection methods: 1) One-Class Support Vector Machine (OC-SVM), 2) Clustering (DBSCAN), and 3) Reconstruction Error (RecErr) thresholding were compared to find the best model for each power transient dataset. Precision rates, recall rates, F1-scores, and accuracies were used to compare model performance. The best models produced total accuracies of 94.50%, 93.63%, and 91.76% for the steady state, ramp up, and ramp down datasets, respectively, or 99.19%, 98.97%, and 97.09% when omitting 1%-severe fault cases. Specifically, the best performing models for the steady state, ramp up, and ramp down cases were the CNNAE-RecErr, DNNAE-DBSCAN, and CNNAE-RecErr, respectively. Recall rates were calculated for various fault types at multiple severity levels. The results show that the Fault Detection Module can identify any type of fault in the system with a delay of a few seconds on average. Additionally, robustness against noisy sensor data was tested, with models maintaining 100% accuracy at various levels of noise, and an illustrative real-time application of the methodology is provided.

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection.

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

Investigating and Reducing the Architectural Impact of Transient Faults in Special Function Units for GPUs

Ensuring the reliability of GPUs and their internal components is paramount, especially in safety-critical domains like autonomous machines and self-driving cars. These cutting-edge applications heavily rely on GPUs to implement complex algorithms due to their implicit programming flexibility and parallelism, which is crucial for efficient operation. However, as integration technologies advance, there is a growing concern regarding the potential increase in fault sensitivity of the internal components of current GPU generations. In particular, Special Function Unit (SFU) cores inside GPUs are used in multimedia, High-Performance Computing, and neural network training. Despite their frequent usage and critical role in several domains, reliability evaluations on SFUs and the development of effective mitigation solutions have yet to be studied and remain unexplored. This work evaluates the impact of transient faults in the main hardware structures of SFUs in GPUs. In addition, we analyze the main overhead costs and benefits of developing selective-hardening mechanisms for SFUs. We focus on evaluating and analyzing two SFU architectures for GPUs (’fused’ and ’modular’) and their relations to energy, area, and reliability impact on parallel applications. The experiments resort to fine-grain fault injection campaigns on an RTL GPU model (FlexGripPlus) instrumented with both SFUs. The results on both SFU architectures indicate that fused SFUs (in commercial-grade devices) require lower area overhead (about 27%) for their integration in GPUs but are more vulnerable to transient faults (in up to 47% for the analyzed cases) and less power efficient (in up to 36.6%) than modular SFUs. Moreover, the reliability estimation shows that Modular SFUs are structurally more resilient than Fused ones in up to one order of magnitude. Similarly, selective-hardening mechanism based on Triple-Modular Redundancy (TMR) shows that coarse-grain strategies might increase the reliability of the overall SFUs under feasible overhead costs.

Centralized event-based protection based on communication infrastructure of medium voltage direct current network

Direct current (DC) distribution systems play a crucial role in integrating various DC systems, renewable energy sources, and power loads. Single-ended protection methods are inadequate for ensuring high-speed selective protection due to the lack of boundary elements, while current differential protection methods can be affected by line-distributed capacitance currents. Therefore, an event-based protection scheme is proposed that uses communication infrastructure and the first derivative of the transient fault current to provide a selective protection system for Medium Voltage Direct Current (MVDC) networks. The scheme utilizes Intelligent Electronic Devices (IEDs) to transmit events to the Intelligent Protection Center (IPC) via a communication network, which compares them to identify the faulty section. The proposed protection scheme is effective in detecting and isolating faults, even under challenging network conditions, as demonstrated by simulation results on a two-terminal topology MVDC distribution network using the PSCAD/EMTDC platform.

An Adaptive Reclosing Scheme for Cross-Line Faults on Double-Circuit Wind Power Outgoing Lines with Shunt Reactors

Wind turbines are vulnerable to negative sequence current injection, the conventional automatic reclosing scheme for wind power outgoing lines, since we may inject negative sequence components into the system and reclose it without distinguishing the nature of the fault through rectification. Moreover, reclosing in permanent faults could induce a secondary impact on the system. To solve the above problems, an adaptive reclosing scheme for cross-line faults on double-circuit wind power outgoing lines with shunt reactors is proposed. Firstly, a new tripping strategy and a single-side partial-phase reclosing method are proposed for multiple types of outgoing line faults, while, simultaneously, phase-to-phase coupling loops are established. Secondly, the criteria of fault nature are established based on the fault phase shunt reactor current and fault phase voltage characteristics, and transient faults are rapidly and accurately distinguished from permanent ones according to the criteria. Finally, the theoretical derivation and simulation experiments are conducted on the PSCAD/EMTDC platform to demonstrate that the proposed adaptive reclosing method is applicable to avoid the injection of negative sequence currents into wind turbines. Meanwhile, the success rate of reclosing for wind power outgoing line is significantly improved and the continuity of power transmission on the wind farm(s) is ensured.

Comprehensive fault diagnosis in UAV-assisted sensor networks: A three-phase automated approach

Ensuring operational safety in an automated working environment is a crucial requirement. In this article, we propose a technique for automated fault diagnosis in unmanned aerial vehicle-based sensor networks (UAV-sensor networks) aimed explicitly at the composite nature of different faults. Our technique consists of 3-stages: (1) UAVs collecting data from sensors and running a modified Z-score statistics to diagnose faulty readings; (2) the UAV cluster head running a multivariate analysis of variance (MANOVA) to identify data faults in cluster members; and (3) utilizing a probabilistic neural network with correlation centroids at the ground control station for further diagnosis of collected UAV data. This approach is intended for progressive improvement in fault diagnosis at different stages of data collection in a UAV-sensor network for critical applications. We conduct an extensive evaluation of our 3-stage approach on the data collected in real-time from the sensors in a physically created UAV-sensor network. The performance of the proposed modified Z-score test improves fault detection accuracy of ∼20.8%, false alarm rate of ∼54.3%, and false positive rate of ∼67.2% with the traditional and existing sensor fault detection approach. The performance of MANOVA to identify the permanent UAV data fault shows fault detection accuracy of ∼10.9%, false alarm rate of ∼44.3%, and false positive rate of ∼48.7% superior to the intermittent and transient faults. Our preliminary results suggest that the proposed approach can handle multiple composite faults and outperforms existing research results.

Analysis of Grid-Forming Inverter Controls for Grid-Connected and Islanded Microgrid Integration

Autonomous grid-forming (GFM) inverter testbeds with scalable platforms have attracted interest recently. In this study, a self-synchronized universal droop controller (SUDC) was adopted, tested, and scaled in a small network and a test feeder using a real-time simulation tool to operate microgrids without synchronous generators. We presented a novel GFM inverter control adoption to better understand the dynamic behavior of the inverters and their scalability, which can impact the distribution system (DS). This paper provides a steady-state and transient analysis of the GFM power inverter controller via simulation to better understand voltage and frequency stabilization and ensure that the critical electric loads are not affected during a prolonged power outage. The controllers of the GFM inverter are simulated in HYPERSIM to examine voltage and frequency fluctuations. This analysis includes assessing the black start capability for photovoltaic microgrids, both grid-connected and islanded, during transient fault conditions. The high photovoltaic PV penetration levels open exciting opportunities and challenges for the DS. The GFM inverter control demonstrated appropriate response times for synchronization, connection, and disconnection to the grid. The DS has become more resilient and independent of fossil fuels by increasing the penetration of inverter-based distributed energy resources (DERs).

Fault Diagnosis Method of Inter-Turn Short Circuit of Permanent Magnet Synchronous Motor Based on Third Harmonic

In order to realize the effective diagnosis of turn to turn short circuit fault of permanent magnet synchronous motor, a method of turn to turn short circuit fault diagnosis of permanent magnet synchronous motor based on third harmonic is proposed in this paper. Establish the system frequency oscillation analysis model of permanent magnet synchronous motor under the condition of turn to turn short circuit, collect the turn to turn harmonic of permanent magnet synchronous motor, then realize the third harmonic detection of permanent magnet synchronous motor under turn to turn short circuit fault, and detect the inertia of permanent magnet synchronous motor system. Based on the transient energy analysis and fault feature extraction, the turn to turn short circuit fault of permanent magnet synchronous motor is diagnosed. The test results show that this method has high reliability and good detection performance, and can improve the output stability and condition monitoring ability of permanent magnet synchronous motor.

On the role of poroelastic stressing and pore pressure diffusion in discrete fracture and fault system in triggering post-injection seismicity in enhanced geothermal systems

Injection-induced seismicity has become one of the most critical challenges for the widespread deployment of Enhanced Geothermal Systems (EGS). In particular, some EGS development projects have led to large, damaging earthquakes that unexpectedly occurred far off the stimulated reservoir region and, in particular, after stopping fluid injection. Yet, the causative mechanisms of these seismicity patterns remain highly elusive. Here, we identify a combination of mechanisms that could explain delayed seismicity in EGS sites by conducting fully-coupled hydromechanical simulations of the hydraulic stimulation of a naturally-fractured granitic reservoir. The model comprises a sparse network of long, variably-oriented fractures interacting with a nearby, critically-oriented fault. The results show that the presence of fractures introduces notable nonlinearities in the flow field and rock deformation and significantly expands the rock volume affected by fluid injection. First, the stimulated fracture network provides highly-permeable conduits for communicating elevated pore pressure over long distances. Second, the anisotropic expansion of fractures generates shear stress that is transmitted almost instantaneously across the reservoir. The pore pressure and stress perturbations can not only cause slip along fractures, inducing (micro)seismicity during injection, but also affect the stability of nearby faults, which may not necessarily be pressurized during injection. The transferred poroelastic stresses can increase or decrease the slip tendency along different fault segments. However, the fault may reactivate only after several months following injection when a progressive pore pressure diffusion modulated by the transient fault permeability evolution brings a critically-stressed fault segment to failure conditions. We also find that the spatiotemporal evolution of seismicity depends largely on the nearby fault orientation, hydromechanical properties, and hydraulic connection with the fracture network, as well as the initial state of stress. We conclude that accurate subsurface characterization and continuous monitoring during and after injection should allow for managing the risks posed by injection-induced seismicity and safely unlocking the immense potential for clean and sustainable geothermal energy.

Applying Particle Swarm Optimization for Investigating the Stability of DC-DC Buck-Boost Converter: A Software Based Approach

ABSTRACT Stability of power converters has always been a major concern when dealing with nonlinear behavior of the system and swarm-based optimization techniques are proven to be handy as they are widely employed. Hence, this study presents an investigative approach to explore the compatibility of applying different variants of Particle Swarm Optimization (PSO) to design an optimized PID controller for DC-DC Buck-Boost converter. This work assesses converter stability through optimization of the PID controller using classical PSO (cPSO), Time-Varying Inertia Weight PSO (TVIW-PSO), Chaotic Descending Inertia Weight PSO (CDIW-PSO), Self-organizing Hierarchical PSO (HPSO) and Random inertia weight PSO (RIW-PSO). The State Space Average (SSA) method is utilized to model and attain the transfer function; eigenvalue analysis and time domain simulation analysis are considered to investigate the stability. Moreover, four objective functions and various performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to assess the system’s efficiency and the outcomes of different approaches are compared extensively. Amongst all, CDIW-PSO showcased better performances e.g. overshoot of 1.08%, settling time 0.000228s and rise time 0.000143s. The findings illustrate algorithm’s capacity to reduce transient and steady-state faults and makes it compatible with more nonlinear applications.

Energy-Constrained Scheduling for Weakly Hard Real-Time Systems Using Standby-Sparing

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state-of-the-art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint.

An adaptive reweighted-Kurtogram for bearing fault diagnosis under strong external impulse noise

In vibration-based fault diagnosis of rotating machinery, strong impulse noise constitutes a widely encountered source of external interference, thereby posing significant challenges to the effective extraction of diagnostic information. In this context, an adaptive reweighted-Kurtogram (ARKurtogram) method is proposed. The bearing transient fault signatures are accurately extracted through two aspects – that is, precise frequency band division and robust band selection indicator. Specifically, we use the dual-tree complex wavelet packet transform (DTCWPT) as the band division tool and propose the sub-bands rearranged and ensemble DTCWPT which addresses the band disorder problem, yields far richer band division patterns and maintains excellent performance of DTCWPT in signal decomposition. Meanwhile, we introduce a robust indicator characterizing periodic fault impulses to accurately select the fault band. In comparison with the classical kurtosis and the advanced reweighted-kurtosis, this indicator is insensitive to strong external impulse noise and its calculation process contains a sliding window averaging approach which makes the calculation entirely automated without human intervention. The validity of the ARKurtogram is investigated with the analysis of vibration signals measured from fault-injection experiments. The comparisons with the classical fast Kurtogram method and the advanced Autogram method highlight the advantages of the method in extracting bearing transient fault signatures from complex signals with strong external impulse noise. It shows high potential in the application of industrial bearing fault diagnosis.

Investigating the impact of transient hardware faults on deep learning neural network inference

SummarySafety‐critical applications, such as autonomous vehicles, healthcare, and space applications, have witnessed widespread deployment of deep neural networks (DNNs). Inherent algorithmic inaccuracies have consistently been a prevalent cause of misclassifications, even in modern DNNs. Simultaneously, with an ongoing effort to minimize the footprint of contemporary chip design, there is a continual rise in the likelihood of transient hardware faults in deployed DNN models. Consequently, researchers have wondered the extent to which these faults contribute to DNN misclassifications compared to algorithmic inaccuracies. This article delves into the impact of DNN misclassifications caused by transient hardware faults and intrinsic algorithmic inaccuracies in safety‐critical applications. Initially, we enhance a cutting‐edge fault injector, TensorFI, for TensorFlow applications to facilitate fault injections on modern DNN non‐sequential models in a scalable manner. Subsequently, we analyse the DNN‐inferred outcomes based on our defined safety‐critical metrics. Finally, we conduct extensive fault injection experiments and a comprehensive analysis to achieve the following objectives: (1) investigate the impact of different target class groupings on DNN failures and (2) pinpoint the most vulnerable bit locations within tensors, as well as DNN layers accountable for the majority of safety‐critical misclassifications. Our findings regarding different grouping formations reveal that failures induced by transient hardware faults can have a substantially greater impact (with a probability up to 4 higher) on safety‐critical applications compared to those resulting from algorithmic inaccuracies. Additionally, our investigation demonstrates that higher order bit positions in tensors, as well as initial and final layers of DNNs, necessitate prioritized protection compared to other regions.

Fault protection of quasi-delay-insensitive pipeline models using efficient coding for asynchronous network-on-chip

<div align="center"><span>One promising approach for creating the chip-level connection of multiprocessing system on chip (MPSoC) is asynchronous logic. However, asynchronous systems are susceptible to errors. In this manuscript, the efficient fault-tolerant (FT) quasi-delay-insensitive (QDI) pipeline modules are designed using a delay-insensitive redundant check (DIRC) coding mechanism. The DIRC coding approach can tolerate single and multi-bit transient faults (TFs) in QDI-pipeline modules. The 4-phase 1-of-n coding approach incorporates DIRC-based QDI pipeline stages to strengthen the asynchronous links against TFs. The DIRC-based QDI pipeline stages are further used as asynchronous links in asynchronous network-on-chip ((NoC) for fault-free communication. The performance metrics like chip area, delay, and power parameters are evaluated in detail against different data widths for both basic unprotected and DIRC-based QDI pipeline modules. <br /> The DIRC-based QDI-pipeline module with 1-of-4 code uses only <2% chip area with a delay of 7.4 ns and power of 117 mW on Artix-7 chip for data width 128. The code rate of the proposed work decreased by 33.33% for both 1-of-2 and 1-of-4 codes in DIRC-based QDI-pipeline modules.</span></div>

Identification of Intermittent Ground Fault Lines in Medium Voltage Shipboard Power Systems

To locate increasingly frequent intermittent ground faults in medium-voltage shipboard power systems, this study proposes a fault branch selection method using wavelet analysis. By analyzing transient fault current characteristics in ship medium-voltage grids, this method utilizes wavelet analysis tools to extract features and obtain modulus maxima of branch currents. Based on a dual threshold detection strategy, the fault branch is then identified. The feasibility and validity of the proposed method are verified through simulations on the Matlab/Simulink platform. The simulation results demonstrate the capability of the wavelet analysis approach to accurately locate intermittent ground faults in ship medium-voltage power systems. This research provides an effective solution for fault branch selection of intermittent grounding faults in ship medium-voltage grids.

Feature Extraction and Diagnosis of Periodic Transient Impact Faults Based on a Fast Average Kurtogram–GhostNet Method

This paper proposes an improved fault diagnosis algorithm that combines a modified fast kurtogram (FK) method with the lightweight convolutional neural network GhostNet. The FK algorithm can adaptively select resonance demodulation bands for envelope demodulation to extract fault features, but it may be disturbed by non-Gaussian noise. Hence, the fast average kurtogram (FAK) method based on sub-band averaging was introduced. This method effectively weakens the impact of pulse noise on the kurtosis graph by splitting the signal into equal-length sub-signals and calculating the average kurtosis value of all sub-signal filters. Simultaneously, to fully utilize the advantages of deep learning technology in feature extraction and classification, this study used the FAK to convert vibration signals from one-dimensional to two-dimensional kurtosis graphs as the input for the GhostNet model. This combination not only achieved accurate fault diagnosis and classification but also showed significant advantages in processing efficiency and resource utilization. The experimental results indicate that the algorithm excelled in extracting features and diagnosing periodic transient impact faults, and compared with traditional methods, it exhibited noticeable improvements in computational efficiency and resource management.

New sparse regularization approach for extracting transient impulses from fault vibration signal of rotating machinery

Fault impulses induced by localized failure is critical for fault diagnosis and degradation prognostics of rotating machinery, however, weak transient fault impulses are always submerged in heavy noise and unrelated components. To overcome this bottleneck issue, a novel approach named smoothing sparse low-rank matrix (SSLRM) associated with asymmetric and singular value decomposition (SVD) penalty regularizers is proposed, for the first time. To be specific, the asymmetric and SVD penalty regularizers are utilized to alleviate the issues of underestimation deficiency of traditional sparse regularization, where the Parseval theorem in wavelet framework and smoothing operation are conducted, so as to enhance the sparsity of the estimated transient impulses while restraining the unrelated interference. Meanwhile, the strict convexity of the cost function is proved theatrically, and the effective optimization algorithm with fast convergence speed based upon the alternating direction method of multipliers (ADMM) is proposed through splitting the optimization function into two simple sub-parts. The simulated case and two experimental cases regarding bearings failure in corn thresher and gear-root crack failure in reducer are investigated for theoretical verification of the proposed approach, the results indicate that diagnosis accuracy and fault impulses amplitude of the algorithm are superior to the state-of-the-art benchmarks.

Self-stabilizing indulgent zero-degrading binary consensus

Guerraoui proposed an indulgent solution for the binary consensus problem. Namely, he showed that an arbitrary behavior of the failure detector never violates safety requirements even if it compromises liveness. Consensus implementations are often used in a repeated manner. Dutta and Guerraoui proposed a zero-degrading solution, i.e., during system runs in which the failure detector behaves perfectly, a node failure during one consensus instance has no impact on the performance of future instances.Our study, which focuses on indulgent zero-degrading binary consensus, aims at the design of an even more robust communication abstraction. We do so through the lenses of self-stabilization—a very strong notion of fault-tolerance. In addition to node and communication failures, self-stabilizing algorithms can recover after the occurrence of arbitrary transient faults; these faults represent any violation of the assumptions according to which the system was designed to operate (as long as the algorithm code stays intact).This work proposes the first, to the best of our knowledge, self-stabilizing algorithm for indulgent zero-degrading binary consensus for time-free message-passing systems prone to detectable process failures. The proposed algorithm recovers within a finite time after the occurrence of the last arbitrary transient fault. Since the proposed solution uses an Ω failure detector, we also present the first, to the best of our knowledge, self-stabilizing asynchronous Ω failure detector, which is a variation on the one by Mostéfaoui, Mourgaya, and Raynal.

Generic Soft Error Data and Control Flow Error Detection by Instruction Duplication

Transient faults or soft errors are considered one of the most daunting reliability challenges for microprocessors. Software solutions for soft error protection are attractive because they can provide flexible and effective error protection. For instance, nZDC [1] state-of-the-art instruction duplication error protection scheme achieves a high degree of error detection by verifying the results of memory write operations and utilizes an effective control-flow checking mechanism. However, nZDC control-flow checking mechanism is architecture-dependent and suffers from some vulnerability holes. In this work, we address these issues by substituting nZDC control-flow checking mechanism with a general (ISA-independent) scheme and propose two transformations, coarse-grained scheduling, and asymmetric control-flow signatures, for hard-to-detect control flow errors. Fault injection experiments on different hardware components of synthesizable Verilog description of an OpenRISC-based microprocessor reveal that the proposed transformation shows 85% less silent data corruptions compared to nZDC. In addition, programs protected by the proposed scheme run on average around 37% faster than nZDC-protected programs.