Fault-tolerant Function Research Articles

Intelligent Vehicle Path Tracking Coordinated Optimization based on Dual-steering Cooperative Game with Fault-tolerant Function

Intelligent Vehicle Path Tracking Coordinated Optimization based on Dual-steering Cooperative Game with Fault-tolerant Function

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.

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.

Steel-ball-type six-dimensional dynamic force measurement platform with fault-tolerant performance

To satisfy the requirement of dynamic vibration testing of aerospace equipment on the ground prior to orbiting in space to guarantee the stable functioning of precision components, a steel-ball-type dynamic force measuring platform is suggested. First, based on two traditional platforms, a novel dynamic force measuring platform with fault-tolerant function, higher load capacity, and higher fundamental frequency is proposed, with its fault-tolerant measurement method theoretically deduced. Next, the study focuses on improving the measuring performance of redundant platforms by transforming conventional hard-connected branches into steel-ball branches. The structural parameters of these branches are examined theoretically, and simulations are used to confirm the theory's accuracy. Ultimately, the prototypes of the redundant steel-ball and hard-connected platforms are fabricated. Comparative experiments demonstrate that the steel-ball platform has superior performance, with static and dynamic measuring errors of 1.2% and 2.3%, respectively, and static and dynamic average coupling errors of 0.3% and 1.5%, respectively. The fault-tolerance function's validity is also confirmed.

A data-driven UUV decision-making test strategy design method

Based on the analysis of UUV control architecture and workflow, a data-driven test strategy design method was proposed to realize comprehensive online inspection of UUV, aiming at the requirements of full-process and full-function commissioning, system-level fault testing, single-step action of the actuator, software boundary testing, emergency control and fault-tolerant function verification of a certain UUV. At the same time, three types of typical faults of software, sensors and actuators are identified, and pseudo-random fault codes are injected into UUV control flow or information flow to realize the test verification of action continuity, function matching and control effectiveness under UUV operating conditions. The engineering application proves that the strategy has clear logic and comprehensive functional coverage, laying the foundation for improving UUV reliability and sailing safety.

Integration of Neuromorphic and Reconfigurable Logic‐in‐Memory Operations in an Electrolyte‐Manipulated Ferroelectric Organic Neuristor

The rapid development of digital technology results in a tremendous increase in computational tasks that impose stringent performance requirements on next‐generation computing. Biological neurons with fault tolerance and logic functions exhibit powerful computing capacity when facing complex real‐world problems, which strikes the inspiration for the development of highly energy‐efficient brain‐like computing. Herein, a novel device architecture, an electrolyte‐manipulated ferroelectric organic neuristor, which emulates biological neurons to perform both neuromorphic and reconfigurable logic‐in‐memory operations in a single cell, is proposed. The interfacial coupling of ions and dipoles in the neuristor contributes to the tunable synaptic behaviors of short‐ to long‐term plasticity. Notably, by virtue of lateral capacitive coupling, the neuristor is effectively controlled by multiple in‐plane gates to achieve heterosynaptic plasticity. An artificial neural network exhibits robust recognition ability with high accuracy of 93.7% in speech recognition, further demonstrating the feasibility of the neuristor for neuromorphic computing. Additionally, reconfigurable logic‐in‐memory operations (OR and AND) are successfully demonstrated in a single device. Therefore, the devices shed new light on the development of more brain‐inspired computing systems in the era of big data.

Toward a Live BBU Container Migration in Wireless Networks

Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processor) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.

Study on Modulation Strategy of Electronic Converters Based on Improved D-NPC Topology for Full Electric Vehicle

The battery is the only power source of full electric vehicles, and the converter plays a key role in power and signal conversion; therefore, the stability and reliability of the converter determine the performance of the whole vehicle system. In order to improve the overall performance of the converter and optimize the function of the ID-NPC (improved diode neutral-point-clamped) topology with power allocation, the two-level topology is improved, and it is also a part of the ID-NPC topology. Based on the ID-NPC topology, the converter level can switch according to the proposed three-level and two-level modulation conversion strategies, which extends the fault-tolerant function of the converter. Finally, a simulation and experimental platform is built to verify the function of the improved topology and the feasibility of the proposed modulation strategy.

A fault-tolerant grid-forming converter applied to AC microgrids

A crucial converter for the islanded operation of AC microgrids is the grid-forming converter. This central converter supplies electrical loads, and assures the distributed generators operation, since the malfunctioning of grid-forming converter can jeopardize the whole microgrid. This paper proposes a bidirectional grid-forming converter with a fault-tolerant functionality applied to islanded AC microgrids using a centralized control architecture. The converter is based on a delta connection of three single-phase voltage source inverters (VSIs) using an H-bridge topology for three-phase three- or four-wire systems. For this latter case, a zigzag transformer is used to provide the neutral conductor for the system. In addition to establishing voltage and frequency references for the microgrid, the converter is capable of changing its configuration from delta to open-delta, and vice-versa, in a smooth mode and without interrupting the microgrid operation. Thus, in case of failure of one of the grid-forming VSIs, the system can keep supplying the microgrid. A control strategy is developed to allow a stable operation in all operating modes. Experimental results are presented with a converter prototype to validate the proposed topology, control and mode of connection.

Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

Switching over technique to fault tolerant function of DC-bus battery in dual inverter motor drive

The dual inverter motor drive feeding an open-end winding PM motor is focused on for mileage improvement and autopilot technologies of hybrid and electric vehicles. Autopilot operation requires the fault tolerant functions which achieve to keep controlling the motor, even if some failure occurs in the system. In this paper, the fault tolerant function of the DC-bus battery in the dual inverter motor drive system is discussed. The system has a battery and a capacitor across the DC-bus of each inverter. The capacitor is utilized to keep operating the motor, instead of the failed battery in the fault tolerant function. In this time, however, it is necessarily to control the capacitor voltage of the failed side inverter at constantly with the space vector modulation (SVM). In particular, it is required to regulate the capacitor voltage of the failed inverter at a half of the battery voltage of the normal condition inverter. Therefore, the capacitor voltage must be discharged immediately after some failure is occurred in the battery of one of the inverters. In this paper, the switching over techniques from the normal condition to the fault tolerant function is proposed, and the proposed technique is examined through several computer simulations.

Fault-Tolerant Function of DC-Bus Power Source in A Dual Inverter Drive System and Its Operation Characteristics

Fault-Tolerant Function of DC-Bus Power Source in A Dual Inverter Drive System and Its Operation Characteristics

Experimental Verification of Fault Tolerant Operation Focusing on DC-Bus Battery Failure in Dual Inverter Motor Drive

Recently, a dual inverter motor drive feeding an open-end winding permanent magnet (PM) motor has been studied, aiming for the improvement of total efficiency and a fault tolerant function of hybrid and electric vehicles. The authors have studied the fault tolerant operation of the DC-bus battery, where the failed inverter is operated only with a capacitor across the DC-bus and a space vector modulation (SVM) is employed to regulate the capacitor voltage. In our previous research, the SVM techniques for the fault tolerant operation in a low-modulation-index have been proposed. However, it was difficult to have fault tolerance in a high-modulation-index case. The voltage margin in the fault situation is limited because the failed inverter is operated with the capacitor. In this paper, the SVM technique to achieve the fault tolerant operation in the high-modulation-index state is investigated. The novel point of this paper is that the proposed technique introduces a field-weakening control in order to reduce the command voltage vector within the controllable voltage region. The proposed technique was verified through experimental tests and its operational characteristics were compared with the normal operation, from the viewpoints of the total harmonic distortion (THD) and the efficiencies of the inverters and the motor.

Forecast of Water Quality along the Luanhe River Line Based on BP Neural Network

In order to understand the changes of water quality in the water, the paper uses the improved BP network of LM algorithm to learn and train the data, and implements the neural network model on the MATLAB platform, and uses the processed samples for the established BP neural network. Learning training, in order to prevent some neurons from reaching the state of supersaturation.By normalizing the data, the trained network is used to predict the water quality indicators of the Luanhe River Line. The results show that it is feasible to evaluate the water quality along the sputum by BP neural network model. The model has strong learning, association and fault tolerance functions. The analysis results and process are close to the thinking process and analysis method of the human brain, which makes the water quality evaluation. The accuracy of the results is greatly improved.

An $\ell_0$ -Norm-Based Centers Selection for Failure Tolerant RBF Networks

There are two important issues in the construction of a radial basis function (RBF) neural network. The first one is to select suitable RBF centers. The second one is that the resultant RBF network should be with good fault tolerance. This paper proposes an algorithm that is able to select RBF centers and to train fault tolerant RBF networks simultaneously. The proposed algorithm borrows the concept from sparse approximation. In our formulation, we first define a fault tolerant objective function based on all input vectors from the training samples. We then introduce the minimax concave penalty (MCP) function, which is an approximation of l 0 -norm, into the objective function. The MCP term is able to force some unimportant RBF weights to zero. Hence the RBF node selection process can be achieved during training. As the MCP function is nondifferentiable and nonconvex, traditional gradient descent based algorithms are still unable to minimize the modified objective function. Based on the alternating direction method of multipliers (ADMM) framework, we develop an algorithm, called ADMM-MCP, to minimize the modified objective function. The convergent proof of the proposed ADMM-MCP algorithm is also presented. Simulation results show that the proposed ADMM-MCP algorithm is superior to many existing center selection algorithms under the concurrent fault situation.

Predictive DTC Strategy With Fault-Tolerant Function for Six-Phase and Three-Phase PMSM Series-Connected Drive System

On the one hand, with the use of direct torque control (DTC) for a six-phase permanent magnet synchronous motor (PMSM) and a three-phase PMSM series-connected system supplied by a six-phase inverter, it is possible to realize fast torque control for two PMSMs. On the other hand, because the number of inverter output voltage vectors (VVs) is increased to 64, it is difficult to divide sectors for traditional switching table DTC (ST-DTC). Furthermore, it is difficult to choose an optimal switching vector to reduce torque and flux ripples of two PMSMs in ST-DTC. A predictive control strategy of torque and stator flux is introduced into a two-PMSM series-connection DTC system, including winding fault, in this paper. Based on the principle of minimum errors of torque and stator flux amplitude and minimum zero-sequence current, an optimal VV is predicted. In order to realize uninterrupted torque control before and after open-phase fault, open-phase winding flux is considered in stator flux, and the inverter VV considering open-phase voltage is constructed in an open-phase control strategy. The experimental results show that the steady-state torque and stator flux ripples of a series-connected drive system are greatly reduced, and the drive system can operate without interruption from failure-free to failure operation.

The research on dynamic properties of the small world neural network based on the synaptic plasticity

The artificial neural network has the ability of the information processing and storage, good adaptability, strong learning function, association function and fault tolerance function. The research on the artificial neural network is mostly focused on the dynamic properties due to fact that the applications of artificial neural networks are related to its dynamic properties. At present, the researches on the neural network are based on the hierarchical network which can not simulate the real neural network. As a high level of abstraction of real complex systems, the small world network has the properties of biological neural networks. In the study, the small world network was constructed and the optimal parameter of the small word network was chosen based on the complex network theory firstly. And then based on the regulation mechanism of the synaptic plasticity and the topology of the small world network, the small world neural network was constructed and dynamic properties of the neural network were analyzed from the three aspects of the firing properties, dynamic properties of synaptic weights and complex network properties. The experimental results showed that with the increase of the time, the firing patterns of excitatory and inhibitory neurons in the small world neural network didn't change and the firing time of the neurons tended to synchronize; the synaptic weights between the neurons decreased sharply and eventually tended to be steady; the connections in the neural network were weakened and the efficiency of the information transmission was reduced, but the small world attribute was stable. The dynamic properties of the small world neural network vary with time, and the dynamic properties can also interact with each other: the firing synchronization of the neural network can affect the distribution of synaptic weights to the minimum, and then the dynamic changes of the synaptic weights can affect the complex network properties of the small world neural network.

ADMM-Based Algorithm for Training Fault Tolerant RBF Networks and Selecting Centers.

In the training stage of radial basis function (RBF) networks, we need to select some suitable RBF centers first. However, many existing center selection algorithms were designed for the fault-free situation. This brief develops a fault tolerant algorithm that trains an RBF network and selects the RBF centers simultaneously. We first select all the input vectors from the training set as the RBF centers. Afterward, we define the corresponding fault tolerant objective function. We then add an -norm term into the objective function. As the -norm term is able to force some unimportant weights to zero, center selection can be achieved at the training stage. Since the -norm term is nondifferentiable, we formulate the original problem as a constrained optimization problem. Based on the alternating direction method of multipliers framework, we then develop an algorithm to solve the constrained optimization problem. The convergence proof of the proposed algorithm is provided. Simulation results show that the proposed algorithm is superior to many existing center selection algorithms.

Extending Emergency Repair Response Times for Railway Track Switches through Multi-Channel Redundancy of Functional Subsystems

A novel track switch concept has been developed at Loughborough University, which allows parallel-channel, fault-tolerant functions for the first time. This paper demonstrates, through mathematical modelling, real-world data and conservative assumptions, that using a multi-channel, fault-tolerant switching concept can allow an increase in switch availability over baseline scenarios. Performance of four existing switch types is analysed for baseline performance using field data. Multi-channel architectures are then analysed across a range of reactionary maintenance regimes. Availability measures are obtained which show the range of possible switch availability against maintenance response times. The most significant improvements occur when maintenance practice is also revised, the novel system offering the option to run to subsystem failure and remain functional. Results indicate that for multi-channel installations, gains in system availability are possible even when emergency response times are set orders of magnitude longer than currently achieved, indicating a signifi cant reduction in ongoing maintenance commitment. The work also demonstrates that the particular choice of subsystem architecture is of low significance.

A fault-tolerant ONVIF protocol extension for seamless surveillance video stream recording

ONVIF (Open Network Video Interface Forum) is an importance industrial standard for the video surveillance field. There are over 8000 ONVIF-compliant IP cameras (i.e., ONVIF NVT) and network video recorders (i.e., ONVIF NVS) from various vendors listed on the ONVIF official website. However, because the current ONVIF specifications do not support any fault-tolerant functions to handle NVS failure, in this paper we propose a fault-tolerant solution for ONVIF NVS and try to minimize the modifications to the current ONVIF specifications in the proposed design. Besides, we also propose an extension to the ONVIF NVT for preventing the loss of captured NVT video data during the failover procedure triggered by NVS failure. Finally, we evaluate the following four performance metrics of the proposed fault-tolerant ONVIF surveillance system: the maximal video local cache time supported by an NVT, the response time for accessing an ONVIF web service, the interrupt duration of video recording due to NVS failure, and the live video black out time observed by a video management system. Since the experimental results are all within the acceptable range for a real fault-tolerant video surveillance system in the real world, the experiences provided by this paper is a good reference for improving the fault-tolerant capabilities of the ONVIF video surveillance systems.