Redundant Design Research Articles

State of Charge Estimation for Lithium-Ion Battery Based on Fractional-Order Kreisselmeier-Type Adaptive Observer

For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equivalent circuit model. Firstly, we constructed a fractional-order battery model that includes pseudo-capacitance and determined the values of the circuit elements offline using the least squares method from actual input–output data based on the driving profile of an automobile. Compared to the integer-order battery model, we confirmed that the proposed fractional-order battery model has higher accuracy. Secondly, we constructed a fractional-order Kreisselmeier-type adaptive observer as an observer that performs state estimation and parameter adjustment simultaneously. Applying the general adaptive law to the battery model results in a redundant design with many adjustable parameters, so we proposed an adaptive law that reduces the number of adjustable parameters without compromising the stability of the observer. The effectiveness of the proposed method was verified through numerical simulations. As a result, the high estimation accuracy and convergence of the proposed adaptive law were confirmed.

Irreversible failure characteristics and microscopic mechanism of lithium-ion battery under high dynamic strong mechanical impacts

Lithium-ion battery is the most widely used battery currently, and its reliability and failure under various extreme working environments are therefore widely concerned. Among them, failures resulting from highly dynamic mechanical impacts are the most threatening and less covered by previous research. In this paper, with a specialized Machette hammer impact test system, the irreversible capacity loss of commercial cylindrical jelly-roll lithium-ion batteries under high dynamic mechanical impact was investigated, the influences of impact strength, impact number, and working temperature are also considered. Through microscopic characterization and finite element simulation, the failure mechanisms of anode, cathode, and separator are revealed, and their respective contributions to battery capacity loss are quantitatively analyzed. All these findings provide a theoretical basis for the redundancy design of batteries for missile electro-mechanical systems and other equipment which are subjected to high dynamic mechanical impact conditions.

Redundancy Design of FADS System for Complex Leading- Edge Aircraft Based on Neural Network

The flush airdata sensing system (FADS) calculates flight parameters such as angle of attack, sideslip angle, Mach number, incoming flow pressure and static pressure based on the pressure measurement of the aircraft surface. It can effectively solve the problem that the leading edge of the pitot tube protruding from the fuselage cannot adapt to the severe aerodynamic heating faced by hypersonic aircraft during the cruise phase, and at the same time meet the aircraft's demand for stealth performance. At present, there is little analysis and research on the use of neural network methods and FADS systems for complex leading edge aircraft. In view of the subsonic/transonic conditions of hypersonic aircraft returning autonomously during the landing phase, the redundancy design and verification of the head FADS system of complex leading edge aircraft are carried out considering the influence of thin leading edge and inlet components. 15 pressure measuring holes are opened in the head of the complex leading edge aircraft. Through a large number of refined numerical simulations, the pressure database of the aircraft under different incoming flow conditions is established, and the typical working conditions are verified by wind tunnel tests. For the complex leading edge aircraft, 4 sets of neural network algorithms are established based on pressure data and redundant design research is carried out, including 1 set of 9-hole algorithm and 3 sets of redundant algorithms. Among them, the 9-hole algorithm has a higher accuracy, with the solution error of the angle of attack within 0.07∘, the solution error of the sideslip angle within 0.3∘, the solution error of the Mach number within 0.0012, and the relative error of the solution of the incoming flow pressure and static pressure within 1.5%. In addition, a system solution process with a certain fault tolerance was established, which can continue to maintain the effective output of the incoming flow parameters in the event of failure of any single pressure measuring hole.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Optimization and performance improvement of distributed data storage in hybrid storage systems

With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper proposes a hybrid storage system model combining local storage and cloud storage resources and discusses a series of optimization strategies for this model. The focus of the research is to improve the overall performance of the system through the design of intelligent data sharding, redundancy and fault tolerance mechanisms, and the application of effective load balancing technology. The effectiveness and superiority of the proposed method are verified by testing and analyzing several typical application scenarios on the experimental platform. The experimental results show that the optimized hybrid storage system not only significantly improves the speed of data access, but also effectively reduces the storage cost, demonstrating its potential in future large-scale data management.

Design and Research of Intelligent Valve Control

The design and research of intelligent valve control system aims to improve the efficiency and accuracy of fluid control in industrial process automation. By integrating advanced sensing technology, communication protocols and intelligent algorithms, the precise control of valve position, pressure and flow rate is achieved. STM32 controller and a variety of sensors are used in the system to realize real-time monitoring and feedback control of valve status. The control strategy based on PID algorithm enables the valve to maintain stable operating performance under complex working conditions. In addition, the safety and reliability of the system are also considered, and the robustness of the system is improved by redundant design and fault detection mechanism. The experimental results show that the designed intelligent valve control system has significant advantages in improving response speed, control precision and energy efficiency, and has a wide range of industrial application prospects.

Robust structured light with efficient redundant codes

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination. This paper studies a general-purposed solution to improve the robustness of SL by projecting a redundant number of patterns. Despite sacrificing the signal-noise-ratio at each frame, projected signals become more distinguishable from errors. Thus, the geometry can be recovered easily. We systematically analyze the redundant SL code design rules to achieve high accuracy with minimum redundancy. Based on the more reliable correspondence cost volume and the natural image prior, we integrate spatial context-aware disparity estimators into our system to further boost performance. We also demonstrate the application of such techniques in iterative error detection and refinement. We demonstrate significant performance improvements of efficient redundant code SL systems in both simulations and challenging real-world scenes.

A Review on the Development of High-Reliability Motor System for Safety-Critical Applications—From Redundancy Design Prospective

A Review on the Development of High-Reliability Motor System for Safety-Critical Applications—From Redundancy Design Prospective

Redundant series cell voltage measurement circuit design of battery management system for functional safety

In most battery management system (BMS) circuit designs, the analog front end (AFE) chip is used to get the series cell voltage of the battery pack. However, it can lead to incorrect calculations by the BMS, causing errors in battery information, failures in protection control, and triggering a battery system fire accident if the AFE chip is abnormal. Unfortunately, the BMS will never know the correct of measured results if it is measured by a single AFE chip. This paper proposes a redundant series voltage measurement system (RSVMS) for the series cell voltage of the battery pack. The original AFE is referred to as the main AFE (m-AFE). The RSVMS can be regarded as redundant AFE (r-AFE). In this paper, the r-AFE consists of a series cell selector and an analog-to-digital (ADC) converter with an isolated communication function to transmit measurement results to the microcontroller unit (MCU) of the BMS. Due to the hardware requirements of functional safety, it does not allow using two identically designed hardware as redundant systems. To satisfy the hardware level of functional safety and to minimize the hardware cost and size, the series cell selector of r-AFE shares the same hardware circuit as the Active Hybrid Equalizer Circuit (A-HEC). The cell selector of A-HEC is composed of the back-to-back MOSFET switch array with a simple ON/OFF function. The MCU of BMS will identify the abnormal of voltage measurement results when the difference of m-AFE and r-AFE is over the threshold. In summary, r-AFE can ensure the accuracy of cell voltage measurement for m-AFE to avoid significant errors when estimating battery information and avoid disaster caused by failure or malfunction of protection functions. In addition, the m-AFE and r-AFE are two independent designs to avoid similar errors caused by identical circuit configurations.

Reliability Analysis of Modular Multilevel Converters in MVDC Applications

In modular multilevel converters (MMCs) designed for medium-voltage DC (MVDC) systems, since the required sub-modules (SMs) in one arm are fewer, the converters cannot afford the same level of redundancy as observed in high-voltage DC (HVDC) applications. In order to ensure high availability of converters in MVDC systems, reliability becomes a challenge and requires different design approaches. Considering the redundancy design, operating modes, and correlation across failures of SMs in one arm, this paper analyses and compares reliability in MMCs for MVDC systems with multiple SM topologies. Under different scenarios in MVDC systems, SM topologies with better performance of reliability have been identified in this research. It is demonstrated that, for a given output voltage in converters, three-level SMs generally provide higher reliability than two-level SMs. By implementing passive and load-sharing operation schemes in converters, the MMC reliability can be improved compared with the conventional active mode. In terms of the correlation between lifetime of SMs in the same arm, the smaller disturbance introduced by one faulty SM promotes the improvement of converter reliability. Also, the correlation between failures of SMs in one arm has greater effects on the reliability of an MMC with higher level of redundancy. The outcomes of this work contribute to the optimisation of converter design and operation with high reliability and low investment for an MMC in MVDC systems.

Redundancy Design and Preventive Maintenance for a Load-Sharing Multiasset System Considering Uncertain Environmental Conditions

Redundancy Design and Preventive Maintenance for a Load-Sharing Multiasset System Considering Uncertain Environmental Conditions

Analysis and Application of Chaotic Genetic Algorithm Based on Network Security in The Research of Resilience of Cluster Networks

With the wide application of UAV, in the actual flight process, UAV needs to calculate the safe path according to its own position, environment, obstacles and other information. Due to the complex and changeable scene and environment of UAV mission execution, it is very important to select an appropriate UAV path planning algorithm. This paper aims at the path planning problem of multiple UAVs in a complex three-dimensional environment to ensure that multiple UAVs reach the mission location from different angles. Taking the chaotic genetic algorithm in network security protection as the main body, the operation difficulty of the algorithm is reduced, and the solution speed and accuracy of the algorithm are improved. The path length obtained by the proposed algorithm is 8.4% less than that of the ABC algorithm, 11.3% less than that of the PSO algorithm, and 4.2% less than that of the BABC algorithm. The system running time of the improved algorithm is also reduced by 27% to 45% compared with other algorithms. In terms of unmanned cooperation, this paper proposes a system capability based on network modeling to improve the cooperative combat capability of multiple UAVs. By establishing a network model, information sharing, collaborative decision-making and collaborative decision-making between drones are realized, thereby improving the effectiveness of the entire system. At the same time, this paper also considers the problem of network survivability. By introducing redundant design and fault recovery mechanism, the robustness and reliability of the system are enhanced.

QCA-based fault-tolerant XOR Gate for reliable computing with high thermal stability

The XOR gate is an essential element in the design of digital circuits due to its versatility and usefulness. The design of XOR gate in this paper is based on Quantum-dot Cellular Automata (QCA) 2D planner technology with no line-to-line intersections. The output amplitude is improved by redundant cell-based design, which also helped reliability and fault tolerance outperform. The proposed XOR gate achieves fault tolerance to single-cell addition and missing-cell defects from 68.48% to 95.33%. In addition, the proposed XOR gate is also fault-tolerant against multiple-cell missing defects, as verified from the simulations. Furthermore, high thermal stability makes the circuit reliable for QCA-based digital design applications. The digital design applications such as 4-bit B2G code converter and a 4-bit parity checker are designed from this XOR gate, utilizing 438 and 414 cells, respectively. This demonstrates its effectiveness in designing fault resilient and reliable circuit designs for various applications.

Scheduling redundancy optimization for precast production to enhance disruption management in prefabricated construction

Redundancy is a highly effective approach for minimizing losses in precast production when faced with inevitable disruptions. However, existing studies have primarily focused on enhancing production redundancy in order to mitigate losses caused by disruptions, without considering the added production costs resulting from decreased efficiency. Besides, the cost-effectiveness of redundancy design may be influenced by the disruption types and their associated risk levels, which has not been thoroughly investigated. In this paper, a redundancy optimization model for precast production is proposed to achieve the trade-off between the redundancy benefits and the additional costs involved. The concept of production scheduling redundancy is firstly defined, and the benefits derived from its implementation are quantified. Additionally, different scenarios with high, moderate, and low occurrence levels of disruptions are designed to optimize the production redundancy in different situations. On this basis, the effectiveness and cost-benefits of redundancy are compared subject to various types and levels of disruptions. Finally, a case study is conducted to illustrate the model. The findings show that the machine breakdown is the most critical disruption in the precast production, and implementing redundancy measures prove to be more cost-effective in situations characterized by higher levels of risk. Besides, the optimal degree of redundancy is related to both the levels and the types of disruptions. The methodology of this paper can provide insights for disruption management in precast production and give full play to the benefits of prefabricated construction.

Research on resilience assessment method of train control on-board system

To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system's reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system's performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.

Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution

Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was formulated using a structured continuous Markov chain (CTMC) and considers the time-to-failure (TTF) distribution of a component as a phase-type distribution (PHD). The model’s versatility and practicality are enhanced because the PHD can represent diverse degradation patterns of the components exposed to varied operating environments. The model provides accurate reliability for a mixed redundant system by advancing the approximate reliability function suggested in previous studies. Furthermore, the model would be useful in system design and management because it provides information such as the nth moment of the system’s lifetime distribution. In numerical experiments on some examples, the mixed redundancy was confirmed to devise a more reliable system than the existing active and standby redundancies, and the improvement effect increased as the number of redundant components decreased. The optimal structure for maximizing the expected lifetime of the system changes depends on the reliability of the components and fault detector/switch.

CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.

Research on space power supply utilizing series-parallel techniques

In the realm of space exploration, maintaining a stable and reliable power supply is crucial for the sustained and uninterrupted operation of artificial satellites. However, the unpredictable and often harsh space environment necessitates a redundant design for space power supplies to enhance overall reliability. In light of this, this paper introduces a full-bridge converter employing a series-parallel topology, which augments input voltage redundancy through a combination of serial and parallel connections. Furthermore, a dual-loop Proportional-Integral (PI) control feedback system has been devised, along with a comprehensive analysis of system stability to ensure the optimal performance of both output voltage and current. To validate the efficacy of the series-parallel topology and the feedback system, a complete circuit is constructed, and extensive simulations are carried out under diverse conditions.

Design, analysis and optimization of a novel redundant (6+1)-degree-of-freedom parallel mechanism with configurable platform

In this paper, a concept of adaptive capture for Autonomous Underwater Vehicles(AUVs) using parallel mechanism with configurable platform is proposed. By leveraging the deformation capabilities of the configurable platform, the mechanism can capture AUVs of various sizes and orientations while also expanding its workspace, thereby increasing the success rate of grasping. Firstly, the mechanism design is conducted, a novel parallel mechanism consisting of three identical chains with a three-fold symmetrical Bricard platform is proposed. Secondly, the kinematics are obtained, based on which the workspace analysis is performed, and numerical verification is conducted. Thirdly, the Jacobian matrix is derived and verified, and the singularity analysis is carried out. Finally, the concept of a high-quality workspace tailored for the AUVs recovery scenarios is proposed. To achieve a maximum high-quality workspace while maintaining a compact mechanism structure, two indicators are proposed to evaluate the volume and shape of the workspace, and a multi-objective parameter optimization of the mechanism is conducted. The optimized mechanism can be effortlessly scaled to cover the target workspace while preserving minimal dimensions and compact structure.

A Radiation-Hardened Triple Modular Redundancy Design Based on Spin-Transfer Torque Magnetic Tunnel Junction Devices

Integrated circuits suffer severe deterioration due to single-event upsets (SEUs) in irradiated environments. Spin-transfer torque magnetic random-access memory (STT-MRAM) appears to be a promising candidate for next-generation memory as it shows promising properties, such as non-volatility, speed, and unlimited endurance. One of the important merits of STT-MRAM is its radiation hardness, thanks to its core component, a magnetic tunnel junction (MTJ), being capable of good function in an irradiated environment. This property makes MRAM attractive for space and nuclear technology applications. In this paper, a novel radiation-hardened triple modular redundancy (TMR) design for anti-radiation reinforcement is proposed based on the utilization of STT-MTJ devices. Simulation results demonstrate the radiation-hardened performance of the design. This shows improvements in the design’s robustness against ionizing radiation.