Random Faults Research Articles

Computation and validation of the Expected Value of Power of Two Terminal Series–Parallel PV arrays

PV arrays are susceptible to various types of failures such as partial shading that can negatively affect their performance and efficiency. Studying the impact of these circumstances in the performance of the PV array is key for the development of more efficient PV systems. In this study, the definition of the expected value of power (EVP) that a PV array can produce under a random failure scenario is revised and improved. An algorithm to compute EVP that minimizes the number of simulations is developed and tested in three different PV arrays. Results show a 96.6% reduction on average of the number of simulations needed to compute the EVP for PV arrays made up of 9 modules. The model is validated through an experiment that reproduces the random fault scenario and determines the mean power produced by the PV arrays. For all three experiments, the computed EVP fits the experimental data with R2>0.995.

Evolution Process of Fault Silica Aerogel under High Temperatures: A Molecular Dynamics Approach.

Building fire will seriously threaten human safety. Silica aerogel with low thermal conductivity and thermal stability as fire-retardant material has been widely used in building fireproof structures. However, the natural fragility of silica aerogel will limit its application. In this work, the effects of faults on the thermal stability of silica aerogel are studied by molecular dynamics simulation with large simulation time (20 ns). Additionally, the atomic model of silica aerogel with random faults is built by a straining structure (tensile strains are 10%, 20%, 30%, and 40%). It is found that when the tensile strain is less than 20%, the silica backbone can remain stable. The effects of faults on the thermal stability can be neglected. The silica backbone thermally vibrates during the heating process. However, when the tensile strain is over 30%, it is observed that the faults will enhance the silica backbone merging. Silica aerogel can be stable under 800 K. It is believed that the results of this study will pave the way for the development of fireproof materials.

A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.

Despite various measures across different engineering and social systems, network robustness remains crucial for resisting random faults and malicious attacks. In this study, robustness refers to the ability of a network to maintain its functionality after a part of the network has failed. Existing methods assess network robustness using attack simulations, spectral measures, or deep neural networks (DNNs), which return a single metric as a result. Evaluating network robustness is technically challenging, while evaluating a single metric is practically insufficient. This article proposes a multitask analysis system based on the graph isomorphism network (GIN) model, abbreviated as GIN-MAS. First, a destruction-based robustness metric is formulated using the destruction threshold of the examined network. A multitask learning approach is taken to learn the network robustness metrics, including connectivity robustness, controllability robustness, destruction threshold, and the maximum number of connected components. Then, a five-layer GIN is constructed for evaluating the aforementioned four robustness metrics simultaneously. Finally, extensive experimental studies reveal that 1) GIN-MAS outperforms nine other methods, including three state-of-the-art convolutional neural network (CNN)-based robustness evaluators, with lower prediction errors for both known and unknown datasets from various directed and undirected, synthetic, and real-world networks; 2) the multitask learning scheme is not only capable of handling multiple tasks simultaneously but more importantly it enables the parameter and knowledge sharing across tasks, thus preventing overfitting and enhancing the performances; and 3) GIN-MAS performs multitasks significantly faster than other single-task evaluators. The excellent performance of GIN-MAS suggests that more powerful DNNs have great potentials for analyzing more complicated and comprehensive robustness evaluation tasks.

A study on multiple types of yard cranes rescheduling in ports considering carbon emission

ABSTRACT This paper investigates the rescheduling problem of multiple oil-driven and electric-driven yard cranes across multiple container yards in semi-automated container terminals under random faults. Efficient fault diagnosis and handling mechanisms are provided to mitigate the effects of faults on the performance of yard operations. At the same time, a rescheduling model is proposed, aiming to minimize weighted carbon emissions and completion time. By analyzing the features of the problem, a tailored adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The effectiveness of the ALNS is verified through problem instances generated from the operational data of the port of Dalian in China. The factors considered in this paper are relevant to actual port operations, thus offering reliable solutions for scheduling and greening of ports.

Testing Error Handling Code With Software Fault Injection and Error-Coverage-Guided Fuzzing

Real-world programs require error handling code to handle various kinds of possible errors. However, these errors just infrequently occur due to special conditions, so error handling code is difficult to test. Coverage-guided fuzzing and software fault injection (SFI) are two common techniques that can test error handling code, but they still have major limitations. Specifically, existing fuzzing approaches generate program inputs guided by code coverage, but many occasional errors (such as insufficient memory) are unrelated to inputs, and code coverage cannot effectively reflect the execution contexts of these errors; existing SFI approaches often inject single or random faults, without exploring fault space or using program feedback. In this paper, we propose a new fuzzing framework named EH-Fuzz, to effectively test error handling code. EH-Fuzz uses a context-sensitive SFI-based fuzzing approach to explore fault space and perform fault injection, guided by a new metric named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">error coverage</i> . We evaluate EH-Fuzz on 9 user-level programs and 6 kernel-level modules, and find 45 new real bugs, 31 of which have been confirmed and fixed. We compare EH-Fuzz to existing fuzzing approaches (including AFL, AFL++, Syzkaller, FIZZER and FIFUZZ), and EH-Fuzz finds many real bugs missed by these approaches with higher testing coverage.

Evaluation and Mitigation of Weight-Related Single Event Upsets in a Convolutional Neural Network

Single Event Upsets (SEUs) are most likely to cause bit flips within the trained parameters of a convolutional neural network (CNN). Therefore, it is crucial to analyze and implement hardening techniques to enhance their reliability under radiation. In this paper, random fault injections into the weights of LeNet-5 were carried out in order to evaluate and propose strategies to improve the reliability of a CNN. According to the results of an SEU fault injection, the accuracy of the CNN can be classified into the following three categories: benign conditions, poor conditions, and critical conditions. Two efficient methods for mitigating weight-related SEUs are proposed, as follows: weight limiting and Triple Modular Redundancy (TMR) for the critical bit of the critical layer. The hardening results show that when the number of SEU faults is small, the weight limiting almost completely eliminates the critical and poor conditions of LeNet-5’s accuracy. Additionally, even when the number of SEU faults is large enough, combining the weight limiting and TMR methods for the critical bit of the critical layer can retain the occurrence rate of benign conditions at 98%, saving 99.3% of the hardware resources compared to the Full-TMR hardening method.

Improved differential fault analysis of Grain128-AEAD

The number of smart devices connected to the Internet has been constantly increasing, and as a result, lightweight cryptography (LWC) has become more important in the past decade. The Lightweight Cryptography (LWC) Project is an initiative taken by the National Institute of Standards and Technology (NIST) to standardize such LWC algorithms. Grain128-AEAD, which was submitted to the NIST LWC project, is an encryption algorithm that provides both confidentiality and integrity assurance. Third-party security analysis of the submitted ciphers is an important aspect of the evaluation of the submission to the NIST LWC project. Although several pieces of existing research, such as the bit-flipping attack, random fault attack, and deterministic random fault attack, have examined the security of Grain128-AEAD, there is still room for improvement in the fault attack models of these studies. This work aims to fill this research gap by analyzing the security margin of Grain128-AEAD against a series of improved differential fault attacks. In this study, we developed a probabilistic random fault attack and applied it to Grain128-AEAD. As an improvement of the existing research, a probabilistic approach can be applied to a more relaxed moderate control attack model. The existing moderate control model assumes the fault to be injected within any bit of a given byte, whereas the faults in our improved approach can be injected within any bits of a two-byte/four-byte segment, thereby relaxing the fault precision. The results indicate that the improved moderate control requires 388 keystreams for the two-byte model and 279 for the four-byte model to identify the target fault locations for implementing a state recovery attack. The relaxed fault attack models presented in this work are more practical to implement; hence, the findings of this research have improved the existing studies and narrowed the current research gap on the fault attack models of Grain128-AEAD. % To maintain consistency in terminology, "Grain-128AEAD" has been revised to "Grain128-AEAD" in both the abstract and the main text. Please confirm this revision.

Robust Routing Made Easy: Reinforcing Networks Against Non-Benign Faults

With the increasing scale of communication networks, the likelihood of failures grows as well. Since these networks form a critical backbone of our digital society, it is important that they rely on robust routing algorithms which ensure connectivity despite such failures. While most modern communication networks feature robust routing mechanisms, these mechanisms are often fairly complex to design and verify, as they need to account for the effects of failures and rerouting on communication. This paper conceptualizes the design of robust routing mechanisms, with the aim to avoid such complexity. In particular, we showcase simple and generic blackbox transformations that increase resilience of routing against independently distributed failures, which allows to simulate the routing scheme on the original network, even in the presence of non-benign node failures (henceforth called faults). This is attractive as the system specification and routing policy can simply be preserved. We present a scheme for constructing such a reinforced network, given an existing (synchronous) network and a routing scheme. We prove that this algorithm comes with small constant overheads, and only requires a minimal amount of additional node and edge resources; in fact, if the failure probability is smaller than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/n$</tex-math> </inline-formula> , the algorithm can come without any overhead at all. At the same time, it allows to tolerate a large number of independent random (node) faults, asymptotically almost surely. We complement our analytical results with simulations on different real-world topologies.

A First Approach on the Fault Impedance Impact on Voltage Sags Studies

An analysis of the fault impedance impact on studies of voltage sags caused by system faults is presented. The simulation of 62 case studies of phase-to-ground faults on 13.8, 69, 138 and 230 kV transmission lines were performed and the voltage of a 380 V sensitive industrial busbar consumer was monitored. These lines are part of the electrical system of the city of Goiania, Brazil. For each case study, different fault positions were simulated by considering resistive fault impedances of 0, 2, 5, 10, 15 Ω or a random fault impedance with a specific distribution function. Long-term simulation scenarios were obtained by the Monte Carlo method and analyzed based on their cumulative distribution functions and probability density curves of voltage sags. This is one major contribution of this work. We found that, the greater the fault impedance, the smaller the average number of voltage sags expected per year at the end-user busbar. However, the behavior of the average number of voltage sags of a given class of magnitude with the fault impedance does not follow a general law.

Co-evaluation of power system frequency performance and operational reliability considering the frequency regulation capability of wind power

With the increasing proportion of renewable energy, the power system inertia decreases, and the operation uncertainty rises. It brings concerns about the system frequency and operational reliability. However, the impacts of the power system frequency performance on the reliability parameters of generation units have not been fully investigated. This paper studies the frequency performance and the operational reliability co-evaluation for power systems considering wind turbines. Firstly, a power system frequency regulation model is established considering the regulation capability of wind turbines. Then, the cluster of equivalent wind turbines is incorporated into the frequency regulation architecture of thermal power units, which accelerates the analysis of frequency performance. Then, the frequency performance of the power system with the participation of wind turbines under the operation uncertainty and the unit random faults is quantitatively analyzed. A frequency-dependent generator reliability parameter model is derived. Next, a multi-time scale co-evaluation framework is proposed to realize the co-evaluation of frequency performance and operational reliability. Case studies are carried out on the modified IEEE RTS-79 system and a provincial power system. Results show that compared with the existing research, the proposed method can obtain the frequency performance and reliability results efficiently.

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Methodology of a hierarchical and automated failure analysis and its advantages

&lt;p&gt;Several industries, particularly the automotive sector, are increasingly incorporating more electronics into their products. As a result, these products are becoming more complex and difficult to analyze. This complexity poses a significant challenge for manufacturers in proving the functional safety of their products. Not only do random faults present risks, but component tolerances can also lead to unexpected safety hazards. Current methods are struggling to keep pace with these challenges. We have identified key issues with existing methods and introduce a new approach that leverages computer automation and a model-based framework to enhance the process. We explain how this new method not only improves upon existing practices but also introduces additional capabilities.&lt;/p&gt;&lt;p&gt;In this paper, we examine methods for proving the functional safety of electronic systems. We begin by identifying the challenges associated with current established methods. Next, we introduce our new approach, which relies heavily on computer assistance and offers novel techniques for conducting broader and more in-depth analyses of these systems. We then explain a new workflow that utilizes this approach. To illustrate its application, we provide a demonstrative example. Our conclusion summarizes our findings and results, and we share our thoughts on potential future developments.&lt;/p&gt;

Topological regularization of networks in temporal lobe epilepsy: a structural MRI study.

Patients with temporal lobe epilepsy (TLE) often exhibit neurocognitive disorders; however, we still know very little about the pathogenesis of cognitive impairment in patients with TLE. Therefore, our aim is to detect changes in the structural connectivity networks (SCN) of patients with TLE. Thirty-five patients with TLE were compared with 47 normal controls (NC) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the brain using a 3.0 T MRI. Then, a large-scale structural covariance network was constructed based on the gray matter volume extracted from the structural MRI. Graph theory was then used to determine the topological changes in the structural covariance network of TLE patients. Although small-world networks were retained, the structural covariance network of TLE patients exhibited topological irregularities in regular architecture as evidenced by an increase in the small world properties (p < 0.001), normalized clustering coefficient (p < 0.001), and a decrease in the transfer coefficient (p < 0.001) compared with the NC group. Locally, TLE patients showed a decrease in nodal betweenness and degree in the left lingual gyrus, right middle occipital gyrus and right thalamus compared with the NC group (p < 0.05, uncorrected). The degree of structural networks in both TLE (Temporal Lobe Epilepsy) and control groups was distributed exponentially in truncated power law. In addition, the stability of random faults in the structural covariance network of TLE patients was stronger (p = 0.01), but its fault tolerance was lower (p = 0.03). The objective of this study is to investigate the potential neurobiological mechanisms associated with temporal lobe epilepsy through graph theoretical analysis, and to examine the topological characteristics and robustness of gray matter structural networks at the network level.

Health state identification of circulating seawater pump-unit in nuclear power plant based on multi-virtual vibration source fusion in the presence of strong data imbalance

The circulating seawater pump-unit (CRF pump-unit) is a crucial equipment that provides cooling water for the secondary circuit of a nuclear power plant (NPP). Its long-term, fault-free operation ensures nuclear power safety and economy. In this study, considering the limited fault-state samples in the historical database and the random fault locations in NPP equipment, a health state identification framework of the CRF pump-unit is proposed, which combines multiple sources of network input and a feature fusion network. The proposed kernelled independent component analysis with reference (KICA-R) is utilized with reference signals to separate virtual sources from multiple raw acceleration signals. It enhances the fault characteristic frequencies related to several subsystems of a complex mechatronic system in the training samples of the network. Based on the newly designed multi-source channel attention module (MS-CAM) that can adaptively allocate fault-sensitive weight coefficients, a health state identification network incorporates the enhanced input features through the virtual sources obtained from KICA-R. By conducting the benchmark analysis on two public datasets and various types of fault simulation tests on the CRF pump-unit simulator, training sets with different levels of data imbalance are constructed, and the optimal network parameters are determined. The comparisons against cICA validate the ability of KICA-R in virtual source separation, and the strong performance of the MS-CAM-based network in identifying different health states of the CRF pump-unit is further demonstrated through detailed comparative analysis.

Intermittent fault diagnosis for electronics-rich analog circuit systems based on multi-scale enhanced convolution transformer network with novel token fusion strategy

Intermittent faults of analog circuits in the core area of electronic systems may induce false alarms of built-in test (BIT) system and even cause catastrophic accidents. Due to notable non-repeatability and high randomness, intermittent faults are arduous to be detected. In order to enhance the safety and reliability of electronic systems, an end-to-end diagnostic framework based on multi-scale enhanced convolution Transformer network (MSECTN) with novel token mixing and fusion strategy has been developed. Intermittent fault is a kind of random fault from global perspective, and there is also abundant local information inherent in the fault interval. Wholly composed of self-attention mechanisms, Transformer network possesses prominent capability of representing global features and modelling distant temporal associations, thus can be applied to identify intermittent faults. Meanwhile, introduce the local perception of convolution operation into Transformer network. One convolution embedding and convolutional multi-head self-attention module are designed to capture more abundant local details. Since the information at single scale cannot sufficiently reflect the circuit status, multi-scale convolution embedding with kernels in different sizes is performed to enrich the feature representation. Moreover, one token mixing strategy incorporating Fourier transformation, pooling operation, and depth-wise convolution is proposed to simultaneously model the frequency correlation and enhance the information interaction among spatially adjacent tokens. In order to excavate more inherent and distinguishing features, a novel token fusion strategy is designed to eliminate the effects of redundant feature information. Experimental results on two typical analog circuits demonstrated that the proposed approach can effectively identify different intermittent faults.

Recovery policies for safe exploration of lunar permanently shadowed regions by a solar-powered rover

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that the rover may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal planners assume a deterministic rover-environment model and do not account for random faults. In this paper, we consider a random fault profile with a known, average spatial fault rate. We introduce a methodology to compute recovery policies that maximize the probability of survival of a solar-powered rover from different start states. A recovery policy defines a set of recourse actions to reach a safe location with sufficient battery energy remaining, given the local solar illumination conditions. We solve a stochastic reach-avoid problem using dynamic programming to find an optimal recovery policy. Our focus, in part, is on the implications of state space discretization, which is required in practical implementations. We propose a modified dynamic programming algorithm that conservatively accounts for approximation errors. To demonstrate the benefits of our approach, we compare against existing methods in scenarios where a solar-powered rover seeks to safely exit from permanently shadowed regions in the Cabeus area at the lunar south pole. We also highlight the relevance of our methodology for mission formulation and trade safety analysis by comparing different rover mobility models in simulated recovery drives from the LCROSS impact region.

Yard Crane Rescheduling under the Influence of Random Fault

In the operation of the imported container area of the container yard, the fault of the yard crane often occurs, and the fault is random and unpredictable, which greatly affects the operational efficiency of the container yard. To improve the operation efficiency of the container yard, this paper studies the rescheduling optimization problem of the multi-container area and multi-yard crane when random faults occur in container lifting operations in the container import area. Considering the different impacts of different fault conditions on the container yard operation, the fault impact judgment mechanism is established. The waiting time of external container trucks and customer satisfaction is considered for yard crane rescheduling. Yard crane rescheduling model after the fault is constructed, aiming at the minimum deviation from the original scheduling scheme. And the AEA (annealing evolution algorithm) algorithm is used to solve it. The effectiveness of magic and the specificity of the algorithm are verified by the analysis of numerical examples in different scales. The research data of Dalian Port is used to carry out experiments, and the experimental analysis of examples in different scales verifies the effectiveness of the model and the scientific nature of the algorithm. Compared with the existing scheme, this scheme is more practical, which can not only give the treatment scheme immediately when the fault occurs but also effectively improve the working efficiency of the container yard and provide a reference for the port to enhance customer satisfaction.

Application of Multimedia Quality Evaluation Relying on Intelligent Robot Numerical Control Technology in New Energy Power Generation System

New energy (NE) power generation technology has become an important means to solve the problems of global energy demand and environmental governance due to its sustainability and cleanliness. However, restricted by climate and other factors, the instability of its power generation capacity has a serious impact on the voltage multimedia quality evaluation of small microgrids, which even causes damage to important equipment. This paper proposed a multimedia quality evaluation method in the new energy power generation system relying on intelligent robot numerical control technology. According to the state characteristics of different units, the expanded representation models of conventional units, wind turbines, and photovoltaic units were established, respectively. Based on intelligent robot numerical control technology, a multimedia quality monitoring model of the new energy power generation system in random production simulation was established. The planned outage condition of the unit, which was ignored by the current stochastic simulation method, has been fully considered to ensure the rationality of the simulation of intelligent robot numerical control technology. The intelligent robot numerical control technology was used to explore the expected production power change of the unit and to improve the calculation speed and accuracy. In this paper, an appropriate wind and solar power generation model was constructed based on the output characteristics of wind and solar. Secondly, the influence of random faults between units on the subsequent loading rate was studied through probability calculation. The start-stop factors of conventional units were comprehensively analyzed. Then, the expected value of unit start-stop times was obtained. The average relative error of the expected value of the power shortage of the new energy electricity generation system (EGS) was 2.45%, and the error was lower than 2.98%. In the intelligent robot numerical control technology in this paper, the planned outage condition of the unit was considered, which showed the correctness and superiority of the multimedia quality evaluation of the proposed model.

An automotive case study on the limits of approximation for object detection

The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance.This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars.

Fuzzy-model-based finite-time control of nonlinear spacecrafts over a distributed sensor network

In this paper, the problem of finite time control on a nonlinear flexible spacecraft system with random faults is addressed. In practical sensor sampling, the signal in the form of a single packet transmission maybe occur some phenomenon, such as data packet loss, random failure. The actuator failures are considered as random signal obeying Markov process. To overcome the difficulty, a finite time fuzzy control scheme is proposed based on a distributed sensor network. Based on the Lyapunov stability theorem, a design method of controllers is given and a stability criterion with satisfying H∞ performance is obtained in mean-square sense. In the end, a numerical example of the nonlinear spacecraft is used to illustrate the effectiveness of the obtained results.