Fault-tolerant Scheme Research Articles

An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing

SummaryThe virtual machine placement for the highly reliable cloud application is considered as one of the challenging and critical issues. To tackle such an issue, this article proposes the enhanced firefly algorithm based virtual machine placement model. But the migration time of the virtual machine placement is high and to reduce the migration time of the virtual machine placement, this article utilizes the K‐means clustering algorithm. In addition, to obtain the optimal cluster for the virtual machine placement, the adaptive particle swarm optimization with the coyote optimization algorithm is employed. The experimental results are conducted for the proposed approach using various measures such as transmission overhead, total execution time, packet size, parallel applications numbers, and virtual machine numbers. The results demonstrate that the proposed method offers improved performance and an optimal virtual machine placement scheme with respect to the various constraint factors. The evaluation exposes that the proposed method offers less execution time when compared to other methods.

Type-B Nussbaum function-based fault-tolerant control for a class of strict-feedback nonlinear systems

This paper studies the fault-tolerant control (FTC) problem of a class of strict-feedback nonlinear systems. First, we put forward a key theorem which shows that type-B Nussbaum functions can be extended to the cases containing multiple Nussbaum functions in the same Lyapunov inequality under certain conditions. Then, by using the techniques of Nussbaum functions and adaptive control, a new fault-tolerant control scheme is proposed. Compared with the previous work, this paper considers unknown time-varying control coefficients and unknown time-varying fault coefficients of actuators. It is proved that all the signals of the closed-loop system are globally bounded and the tracking error converges to zero asymptotically. Finally, simulations are provided to verify the effectiveness of the proposed control scheme.

A Fault-Tolerant Strategy for Three-Level Flying-Capacitor DC/DC Converter in Spacecraft Power System

With the development of space exploration, high-power and high-voltage power systems are essential for future spacecraft applications. Because of the effects of space radiation such as single event burnout (SEB), the rated voltage of power devices in converters for a spacecraft power system is limited to a level much lower than that for traditional ground applications. Thus, multi-level DC/DC converters are good choices for high-voltage applications in spacecraft. In this paper, a fault-tolerant strategy is proposed for a three-level flying capacitor DC/DC converter to increase the reliability with minimal cost. There is no extra hardware needed for the proposed strategy; the fault tolerance of the converter is only achieved by changing the software control strategy. A stage analysis of the proposed strategy is provided in detail for different fault locations and ratios between the input and output voltage. Finally, a simulation model and prototype are built to verify the effectiveness of the proposed strategy.

Fault-Tolerant Attitude Tracking Control Driven by Spiking NNs for Unmanned Aerial Vehicles.

In this article, we proposed a novel fault-tolerant control scheme for quadrotor unmanned aerial vehicles (UAVs) based on spiking neural networks (SNNs), which leverages the inherent features of neural network computing to significantly enhance the reliability and robustness of UAV flight control. Traditional control methods are known to be inadequate in dealing with complex and real-time sensor data, which results in poor performance and reduced robustness in fault-tolerant control. In contrast, the temporal processing, parallelism, and nonlinear capacity of SNNs enable the fault-tolerant control scheme to process vast amounts of sensory data with the ability to accurately identify and respond to faults. Furthermore, SNNs can learn and adjust to new environments and fault conditions, providing effective and adaptive flight control. The proposed SNN-based fault-tolerant control scheme demonstrates significant improvements in control accuracy and robustness compared with conventional methods, indicating its potential applicability and suitability for a range of UAV flight control scenarios.

Study on Distributed Power-Collection and Cascaded Boosting-Voltage Topology and Strategy for Offshore DC Station

Different from traditional concentrated power-collection and boosting-voltage (CPCB) topology adopted by HVDC station, this paper adopts a distributed power-collection and cascaded boosting-voltage (DPCB) topology for offshore DC station to collect DC power and boost DC voltage, which consists of multiple cascaded single active bridges (SABs). It has merits of strong DC-voltage boosting ability, low design difficulty and cost, and low system error rate. Three aspects are studied as follows. First, DPCB topology with its circuit equations is deduced step by step, and its operational mechanism is analysed under phase-shifting control method further. Second, PI control strategy is designed based on system small signal model, and system performance is analyzed by Bode diagram. Third, fault tolerance strategy with predicted power ratio is designed to keep DPCB station from breakdown when some SABs and generators are out of order, and enormous power difference occurs due to the factors of wind turbine location and windward. Finally, the feasibility and effectiveness of designed DPCB station with its strategy are verified by experiment results.

Fault Tolerant Control via Input-Output Linearization Method for LED-Driver Using a Boost Converter

This paper proposes a method based on an input-output linearization controller with a nonlinear adaptive observer, in order to achieve both an effective light-emitting diode (LED) current tracking and an actuator fault tolerant controller strategy for a LED-driver, using a boost converter. The partial fault is presented as a Loss of Effectiveness (LoE) in the embedded control target by considering that it generates a faulty Pulse-Width Modulation (PWM) signal. Also, faults of energy storage components in the power system are considered as actuator partial faults. An internal stability analysis is presented to ensure the feasibility of the nonlinear controller design. The nominal feedback controller is able to compensate for the nonlinearity of the system exactly, thus yielding a linear control loop. Furthermore, a nonlinear adaptive observer is considered for fault estimation. When the actuator fault is detected and estimated correctly, fault accommodation and reconfiguration strategies are performed to reduce the fault’s effect. The controller and observer gains are tuned using genetic algorithm techniques to have a desired closed-loop and fault estimation error response. Finally, simulations results are done in order to illustrate the effectiveness of the proposed methodology.

Reliability Evaluation of Wind Power Converter Under Fault-tolerant Control Scheme with Quantification of Temperature and Humidity Effects

Reliability Evaluation of Wind Power Converter Under Fault-tolerant Control Scheme with Quantification of Temperature and Humidity Effects

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Simultaneous Multiplicative and Additive Actuator Faults Estimation‐Based Sliding Mode FTC for a Class of Uncertain Nonlinear System

In this article, an adaptive sliding mode fault tolerant control (FTC) is improved in the case of uncertain nonlinear system which is affected by both multiplicative and additive faults in actuator. Especially, when the nonlinear system is modeled by Takagi–Sugeno (T‐S) fuzzy system with local nonlinear model. The main contribution of this paper is developing a model of multiplicative faults, which offers a more realistic dynamic evolution of the actuator degradation. The degradation process is modeled by Wiener process and estimated by the maximum likelihood estimation (MLE). Sliding mode observer (SMO) is conceived to realize the additive actuator faults using convex multiobjective optimization. On these bases, the estimated multiplicative and additive actuator faults are used to design the adaptive sliding mode controller (SMC). Finally, the proposed fault‐tolerant control scheme is demonstrated by the results of inverted pendulum system simulation.

A Secure Cloud-Edge Collaborative Fault-Tolerant Storage Scheme and Its Data Writing Optimization

A Secure Cloud-Edge Collaborative Fault-Tolerant Storage Scheme and Its Data Writing Optimization

Partial State Feedback MRAC Based Reconfigurable Fault-Tolerant Control of Drag-Free Satellite With Bounded Estimation Error

Aiming at the ultra-precision and ultra-stable requirements of drag-free control in space detection missions, a multivariable model reference adaptive control (MRAC) scheme is proposed in this paper based on partial observation state, to provide adaptive suppression of uncertain disturbances and improve detection accuracy. The MRAC scheme utilizes model output matching with partial state containing uniform and bounded observation error, and estimates the unknown state parameters through the adaptive law and high-frequency gain decomposition. In response to the actuator bias fault of the drag-free satellite, a set of state error iterative convergence sequence in the actuator loop is established based on the sequence Lyapunov analysis, which is further introduced into the reconfiguration control input to achieve the fault-tolerant target without changing the nominal controller. Through the Lyapunov stability analysis, the convergence of the closed-loop system states in the presence of actuator faults and observation errors is obtained. Numerical simulation results for a 6-DOF drag-free control system demonstrate the effectiveness of the proposed MRAC-based reconfigurable fault-tolerant control scheme.

Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor Networks

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment of VSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTAS-ET). The scheme combines fault-tolerant and aggregate signcryption, which not only makes up for the deficiency of low security of aggregate signature, but also makes up for the deficiency that aggregate signcryption cannot tolerate invalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supports unbounded aggregation when the total number of signcryptions grows dynamically. In addition, this scheme supports heterogeneous equality test, and realizes the access control of private data in different cryptographic environments, so as to achieve flexibility in the application of our scheme and realize the function of quick search of plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, we conduct strict and standardized experimental operation and performance evaluation, which shows that the scheme has better performance.

Open-circuit and Short-circuit Fault Diagnosis and Fault Tolerant Strategy for Non-isolated Three-level Boost DC-DC Converters for Photovoltaic Energies Applications

Open-circuit and Short-circuit Fault Diagnosis and Fault Tolerant Strategy for Non-isolated Three-level Boost DC-DC Converters for Photovoltaic Energies Applications

Fault-Tolerant Strategy for the MMC-Based PV System With Faults Detection and Converter Reconfiguration Using Permutation Algorithms

Fault-Tolerant Strategy for the MMC-Based PV System With Faults Detection and Converter Reconfiguration Using Permutation Algorithms

Unified neuroadaptive fault-tolerant control of fractional-order systems with or without state constraints

The problem of controlling fractional-order nonlinear systems remains interesting and challenging due to the powerful and hereditary memory features of such systems. In this paper, we investigate the adaptive tracking control problem for a class of fractional-order nonlinear systems with or without state constraints. The tan-type barrier Lyapunov function (BLF) is adopted for the first time in fractional Lyapunov direct method. Such technique allows for the accommodation of the situations with and without state constraints in a unified framework. Moreover, neural network (NN) is utilized to reconstruct the sophisticated nonlinear functions arising from the fractional-order differentiation of the virtual controller, resulting in a neuroadaptive fault-tolerant control scheme. It is shown that, with the proposed unified control, all the closed-loop signals are semiglobally ultimately uniformly bounded whether state constraints are imposed or not. Both theoretical analysis and numerical simulation confirm the effectiveness of the developed approach.

Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.

A fault-tolerant sensor fusion in mobile robots using multiple model Kalman filters

Accurate localization is crucial in the navigation of mobile robots. However, in other circumstances, single-sensor localization faces different challenges, including software and hardware problems or data outages. Sensor fusion is used in most autonomous vehicles (including aerial and ground vehicles) to overcome such challenges. In this paper, the localization of a mobile robot is studied in the presence of sensor faults. The mobile robot has two sensors: two Inertial Measurement Units (IMU) and wheel encoders. Regarding the fault-tolerant scheme, measurements of both sets of sensors are fused using an Interacting Multiple Model (IMM) Kalman filter based on both unscented and extended Kalman filters (UKF and EKF). UKF and EKF-based IMM are chosen for this study since the dynamic model of the localization is highly nonlinear. Regarding contributions, it should be noted that this scheme eliminates the need to model every single fault scenario and use an additional sensor to oversee the performance of the sensing system. Also, comparing this method with similar approaches adopted by other studies shows better performance regarding the cost of computations and RMSE. To evaluate performance, the outputs of the proposed filters are simulated and compared for different trajectories where the data of each sensor is intentionally corrupted to observe the fault detection capability. Simulations are performed for different trajectories and noises to demonstrate this method’s efficiency in different situations. In addition, the results of unscented and extended Kalman filter-based IMM are compared in terms of error and computational costs to evaluate their performance. Overall, simulation and experiments indicate accurate 3D estimations in all cases. Moreover, designated weights vividly show that sensor fault detection is achieved by both unscented and extended IMM Kalman filters, which enable complete fault isolation consequently. This approach provides mobile robots with a reliable and straightforward sensor fault detection and localization solution.

Fault-Tolerant Thrust Allocation with Thruster Dynamics for a Twin-Waterjet Propelled Vessel

The availability of the propulsion system is of primary importance to ensure safe and stable operations of marine crafts, both during transit and station keeping. Diminished propulsion efficiency could impair the ability of a vessel to maintain speed and course and possibly lead to a drifting craft. The waterjet’s propulsion efficiency is affected by several factors such as cavitation, erosion, vibration and noise emission. This paper addresses the design of a fault-tolerant thrust allocation algorithm able to maintain the seaworthiness of a twin-waterjet marine craft in the presence of a severe power loss in one of the waterjets. The proposed solution combines a load torque estimator with an optimization routine that accounts for the power limits when a waterjet is subject to a power loss. This prevents faults from quickly escalating into a complete failure of the waterjet due to excessive power demands. Two simulated case studies including zig-zag path following and sideways movements are presented to demonstrate the effectiveness of the fault tolerant control thrust allocation strategy.

Spike-Event X-ray Image Classification for 3D-NoC-Based Neuromorphic Pneumonia Detection

The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency.

A novel compound fault-tolerant method based on online sequential extreme learning machine with cycle reservoir for turbofan engine direct thrust control

Sensors are the primary information source of the aeroengine control system, their measurement accuracy is closely related to whether the engine can operate safely and efficiently. Aiming at the direct thrust control system of aeroengines, this paper proposes an intelligent prediction algorithm combining feedforward and recurrent networks and a fault-tolerant control strategy combining analytical redundancy and controller switching. First, the cycle reservoir with regular jumps is introduced into the online sequential extreme learning machine. The CR-OSELM is developed, which maps the inputs from the low-dimensional space to the high-dimensional space. Then the CR-OSELM is adopted to build the sensor measurement prediction module, and identify and reconstruct the sensor bias and drift fault. Furthermore, in allusion to the rotor speed sensor and temperature/pressure sensor faults, analytic redundancy and controller switching strategies are designed respectively, realizing the fault-tolerant control of thrust feedback. The main contribution of this paper is to come up with the CR-OSELM algorithm with the ability to save historical input information, which overcomes the limitations of traditional feedforward neural networks in identifying time series. And the compound fault-tolerant control scheme is put forward based on the fault characteristics and their impact on the direct thrust control system, which can deal with different types of sensor faults. Simulations are performed on some benchmark datasets and a turbofan engine model to investigate the time series prediction accuracy, sensor fault diagnosis, and thrust fault-tolerant control effect. The results show that the system can detect various faults rapidly, realize the analytic redundancy with high accuracy, reduce the influence of sensor faults and restore the engine performance.