Reliability Design Of System Research Articles

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Friction-induced vibration reliability and sensitivity analysis of a braking system

In this study, reliability and sensitivity analyses are performed for the behavior of friction-induced vibration based on automotive braking systems. In this paper, a friction-induced vibration model of the braking system is developed using Newton’s second law. The accuracy of the vibration model is verified through experiments. The displacement response of the braking system is theoretically derived by convolution integration. The function of maximum displacement is obtained by solving the stationary point of displacement response. Based on this function, the limit state function of the maximum limit of friction-induced vibration displacement is established, and the reliability and sensitivity of the friction-induced vibration of the braking system are evaluated by using the Mean Value First Order Second Moment (MVFOSM) method. The results show that the reliability of the braking system increases with the increase of the mathematical expectation of the equivalent stiffness and decreases with the increase of the mathematical expectation of the system friction coefficient, normal force, and damping ratio. In addition, the sensitivity of the reliability varies significantly with the mathematical expectation of the system friction coefficient and damping ratio. Therefore, the system friction coefficient and damping ratio are the key parameters for the reliability design of the braking system.

A method of automating the process of calculating reliability indicators of software systems and their components

The existing means of calculating reliability indicators of software systems are analyzed. It has been established that to determine the reliability indicators of software systems, it is advisable to use the structural-logical analysis of reliability block diagrams, since it clearly and most adequately reflects the process of calculating the reliability indicators of the software system as a whole and its components in particular. Despite the external simplicity of such an analysis, conducting it is not a trivial task, because even building the condition of technical system operability is a difficult task, especially in the case of the presence of many elements with various connections between them, when solving which manually there is a very high probability human error. Also, the construction and visualization of the graph of states / transitions is a nontrivial task, since the number of possible states of the software system depending on the number of elements grows exponentially, and, in turn, increases the complexity of the system of differential equations, the solution of which makes it possible to calculate the necessary reliability indicators. It was determined that the process of reliability design of complex software systems in general, and their components in particular, requires automation of all its stages, starting from the compilation of the reliability block diagram (RBD), and ending with the visualization of the obtained results. A method of automating the process of calculating the reliability indicators of software systems and their components has been developed, which consists of eight steps and, unlike the existing ones, allows the designer to intuitively enter not only input data about the structure, but also the software architecture itself from the point of view of its reliability, and also automates all stages of calculating reliability indicators, from the stage of constructing a reliability block diagram to the stage of finding the distribution of probabilities of the software system being in all possible states. The proposed method makes it possible to use in various combinations the methods, lgorithms and software tools used for the reliability design of software systems and to choose from them the most adequate to the needs of the user in a specific situation. The use of the developed method makes it possible to reduce the influence of the human factor and the probability of making an error in the process of calculating reliability indicators of software systems at all stages of reliability design and to reduce its time by at least 21 %.

A Three-State Space Modeling Method for Aircraft System Reliability Design

Reliability is an inherent attribute of a system through optimal system design. However, during the aircraft system development process, the reliability evaluation and system function design efforts are often disconnected, leading to a divide between reliability experts and system designers in their work schedule. This disconnect results in an inefficient aircraft system reliability optimization process, known as the “two-skin” phenomenon. To address this issue, a three-state space model is proposed. Firstly, an analysis was conducted on the relationship between the system function architecture developed by the system designers and the reliability evaluation performed by the reliability experts. Secondly, based on the principle of function flow, the state of failure was categorized into “physical failure” and “non-physical failure”. Additionally, a new state of “function loss” was introduced as the third state for the system, in addition to the traditional states of “normal” and “faulty”. Thirdly, through the state of “Function loss”, an effective integration of system fault modes and function modes was achieved, leading to an optimized system reliability model. A three-state space modeling method was then developed by transforming the system function architecture into a system reliability model. Finally, this new model was applied to an aircraft’s rudder and fly-by-wire control system. The results demonstrate that the function architecture at the design stage of the system can be accurately transformed into the new three-state space model. The structure aligns closely with the function architecture and can be effectively utilized in quantitative system reliability calculations. In this way, the process of ensuring system reliability can be seamlessly integrated into the system optimization design process. This integration alleviates the issue of disjointed work between reliability experts and system designers, leading to a more streamlined and efficient aircraft system optimization process.

Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design

To effectively realize the reliability design of engineering structural system, a generative adversarial surrogate modeling (GASM) concept is proposed by innovating generative adversarial theory into surrogate modeling methods, which realize the aerospace engineering structural system reliability design adversarial modeling. In this concept, the surrogate model is adopted as a basis function to describe the functional relationship between input variables and output response; the generative adversarial theory is employed to obtain hyper-parameters of basis functions by continuous confrontation and evolution between the generation model and discrimination model. Under the GASM concept, the generative adversarial polynomial chaos expansion (GAPCE) method is developed to achieve aerospace engineering structural system reliability design. The effectiveness of presented GAPCE is verified by three examples including the nonlinear functions approximation, reliability design of flap deflection angle, and reliability estimation for turbine blisk radial deformation. The reliability level of flap deflection angle and turbine blisk radial deformation are 0.9998 and 0.9984, when the allowable values are 9° and 1.9222 × 10−3 m. Besides, the introduced approach possesses advantages of modeling performance (i.e., modeling accuracy and efficiency) and simulation properties (i.e., simulation precision and efficiency) by comparing with various methods. The efforts of this paper can provide a valuable reference for the operational safety of flap systems and engine structures and the development of reliability design theory.

Design of outdoor LED ground display system in major scenes

AbstractLED ground display system, as an important outdoor display system in major scenes such as some international performance events or interactive display system, has brought spectacular and gorgeous artistic effect to the audience. According to the customized design and the effort we made, this world class super large‐scale outdoor display system has realized successfully at the beginning of the year 2022 in Beijing. This LED ground display system has many innovation in product design, control system design, and field implementation especially for the need of super high reliability, high environmental suitability, convenient installability, 4,500 m2 super large display area, very complex system integration, and long service time. This paper indicates the detail introduction of the ground display system, specially focuses on the high reliability product design, high display performance design, system reliability design, field implementation, and actual verification results of the semi‐circular display area on both sides of the stage. Meanwhile, some simulation technologies are used in this design related to optics, mechanics, and so on. Furthermore, under the special application environment, to ensure the reliability of the outdoor display system, a dynamic screen preheating method according to temperature change is proposed and proved effectively by practice.

Integrated availability importance measure for multi-state complex systems analysis

As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the system.

Research on capacity characteristics and prediction method of electric vehicle lithium-ion batteries under time-varying operating conditions

Electric vehicles (EVs) are being increasingly used today but the safety of their battery system remains an issue. Therefore, a clear understanding of the degradation of EV batteries and the correct prediction of their remaining useful life (RUL) is highly desirable. To solve this problem, firstly, the degradation of EV LIBs at different temperatures and discharge rates are studied experimentally. Then, based on the testing results, the output performance and capacity degradation characteristics of LIBs operating under different conditions are analyze, and a new degradation model is proposed to more accurately predict the capacity degradation of EV LIBs. The research discloses that although the available capacity of LIBs generally decreases with the increase of charge-discharge cycles, at the initial stage of battery use, the available capacity of LIBs are show a transient increase with the increase of charge-discharge cycles. Additionally, the temperature has a significant impact on the available capacity of LIBs, and the alternating changes in ambient temperature can accelerate the degradation of LIBs. Moreover, the innovative degradation model proposed by this paper has higher prediction accuracy than the traditional degradation model, for example, the maximum prediction error given by the proposed model is only 1.17 Ah, which corresponds to a relatively error of 2.34 %; by contrast, the maximum prediction error of the traditional dual exponential model is −5.59 Ah, which corresponds to a relatively error of 11.18 %. These new findings are helpful to the future reliability design of the EVs battery systems.

Bayesian support vector machine for optimal reliability design of modular systems

In a modular system, uncertainties will spread among coupled modules and cause system failure. To cope with this issue, the reliability-based design optimization (RBDO) of modular systems came into being. However, the solution of this design task is a nested triple-loop process, making the computational burden unaffordable for real-world systems. Thus, this paper endeavors to effectively mitigate this computational effort. The individual module feasible approach is first proposed to tackle the coupling effects of modules, whereby, the original optimization problem is converted into a conventional one. Then, the Bayesian-inference-based support vector machine is utilized to build the alternative model for the actual probabilistic constraint function, in the augmented reliability space. The alternative model is constructed using small number of model evaluations, which possesses enough precision everywhere in the augmented confidence region. Finally, the optimal decision scheme is obtained by solving the formulated conventional RBDO using the alternative model. The performance of the proposed method is investigated using several examples.

Improved GA and Its Application in Performance Optimization of Electronic Components

In order to improve the performance optimization effect of electronic components, this paper uses the improved GA to construct the performance optimization system of electronic components. Moreover, this paper proposes an optimized reliability allocation process method based on cost function, which further optimizes the reliability design of electronic circuit systems. In addition, this paper conducts a sensitivity analysis of the electronic circuit system and compares and analyzes the evaluation results of component sensitivity and comprehensive importance based on simulation. Finally, this paper compares and demonstrates the different influences of various components in the electronic circuit system on the whole system. The experimental research shows that the performance optimization system of electronic components based on the improved GA proposed in this paper has a certain effect on improving the performance of electronic components.

Vectorial surrogate modeling method for multi-objective reliability design

To improve the computational cost and accuracy for the multi-objective reliability design of complex structures, a synchronous modeling concept is proposed by introducing matrix thought into the surrogate modeling methods, the purpose of which is to realize the transformation from single-objective reliability design to comprehensive (multiple objectives) reliability design. With respect to the synchronous modeling concept, vectorial surrogate modeling (VSM) method is developed to synchronously establish an overall model of complex structures with multiple objectives. Besides, the types of examples including a numerical example and four engineering cases are adopted to highlight the abilities of the developed VSM method in accuracy, computational efforts and capability. It is illustrated that (1) the developed VSM method is workable in the synchronous modeling and comprehensive reliability design of complex structures with many objectives; (2) the proposed VSM method has the superior performances in both computational efficiency and accuracy for high-dimensional nonlinear problems, by comparison of methods; (3) By comparing with the analytical approaches, the efficient results based on computational times and elevating the performance are provided using the proposed method for highly nonlinear problems; (4) the comprehensive reliability level of turbine blisk deformation, stress and strain is 0.9974 obtained by the VSM method, by considering the effect of fluid-thermal-structural interaction, when the allowable values are 4.3698 × 10−3 m, 1.0389 × 109 Pa and 6.4298 × 10−3 m/m (determined by 3-sigma level); (5) the modeling and simulation efficiencies of the VSM method are improved by 42.56% and 0.31% relative to the RSM. The VSM method are validated to hold high accuracy and high efficiency in modeling characteristics and simulation performance of high-dimensional structural reliability problems. The efforts of this study are to provide a promising synchronous modeling concept and methodology with the VSM method for the multi-objective reliability design of complex systems, which contributes to enrich mechanical reliability theory.

빅데이터 기반 시스템의 사용 패턴을 고려한 신뢰도 최적 설계 프레임워크

Purpose: This study presents a framework for the robust reliability design of a multi-functional system by considering various usage patterns of an existing system.<BR>Methods: The framework consists of four stages: 1) identifying the critical component in a multi-functional system, 2) collecting and analyzing data from users, 3) designing for optimal system reliability, and 4) validating system reliability in various operating environments. Moreover, the framework includes failure mode and effects analysis (FMEA), big data on system usage, the redundancy allocation problem (RAP), and the genetic algorithm (GA).<BR>Conclusion: By using a framework that considers the usage pattern of a system, it was possible to achieve target reliability and a robust design for various usage patterns. Compared to the usage patterns experienced by many users in the existing structure, it was confirmed that most users experienced few (or no) failures. In addition, the framework was robust during the verification stage.

Reliability Uncertainty Analysis Method for Aircraft Electrical Power System Design Based on Variance Decomposition

As a safety critical system, affected by cognitive uncertainty and flight environment variability, aircraft electrical power system proves highly uncertain in its failure occurrence and consequences. However, there are few studies on how to reduce the uncertainty in the system design stage, which is of great significance for shortening the development cycle and ensuring flight safety during the operation phrase. For this reason, based on the variance decomposition theory, this paper proposes an importance measure index of the influence of component failure rate uncertainty on the uncertainty of power supply reliability (system reliability). Furthermore, an algorithm to calculate the measure index is proposed by combining with the minimum path set and Monte Carlo simulation method. Finally, the proposed method is applied to a typical series-parallel system and an aircraft electrical power system, and a criteria named as “quantity and degree optimization criteria” is drawn from the case study. Results demonstrate that the proposed method indeed realizes the measurement of the contribution degree of component failure rate uncertainty to system reliability uncertainty, and combined with the criteria, proper solutions can be quickly determined to reduce system reliability uncertainty, which can be a theoretical guidance for aircraft electrical power system reliability design.

Air Starting System Reliability Uses Reliability Requirement Setting (RRS) Model and Management Action Selection (MAS) Method

Interpretation of the reliability can be used as a quality measurement of operational system of safety. We propose maintenance methodology based on reliability as a solution for predicting reliability system. To make a convenience system appropriate with regulation, we begin to define the three reliability scopes, they are: interval reliability, component reliability, and system reliability. Proposal of Engineering Calculation is the RRS Model for predicting design of component reliability and MAS Method to correct reliability caused by real operation. MAS method consists of harmonization of maintenance schedule, resetting or hazard or overhaul, revitalization (component reliability improvement), and innovation (component replacement with actual part). Case study to implement these model and method based on Air Starting System of Tanker. Predict of system reliability design is 0.438044295. To obtain reliability based on regulation, it is necessary to do overhaul once in every five years. Innovation and revitalization for valve follow procedure of MAS method also done in every five years. &#x0D;

Reviews and Challenges in Reliability Design of Long March Launcher Control Systems

This paper reviews the development of reliability design technology as well as challenges encountered thus far regarding control systems for long march launch vehicles (LMLVs). Along with the high frequency of space launches, the target of “increasing the success rate by one percentage point” has been proposed by the China Academy of Launch Vehicle Technology for LMLVs. In this context, the features of the representative launchers in the reliability design of control systems are reviewed first, especially from an industrial perspective. Thereafter, the techniques applied by LMLV control systems are presented using a five-level hierarchical architecture: fundamentals, self-repair, multiple module redundancy, fault-tolerant control, and fault-tolerant guidance. This is followed by a detailed discussion on the reliability design in LMLV navigation, guidance, control, and avionics subsystems. Finally, the challenges are addressed, which encompass the cost-oriented reliability design, the fault-tolerant control to suppress out-of-tolerance behaviors, and the fault-tolerant guidance to replan the mission autonomously under critical faults. The techniques under development for LMLVs in these areas are also summarized.

Percolation Analysis of Brain Structural Network

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

Numerical simulation of heat transfer performance and reliability design of road snow-melting system based on inorganic heat pipe

In order to study the optimal design parameters of the heat pipe system in the heat pipe snow-melting road structure, a finite element analytical model of the snow-melting road is established based on the heat transfer theory. And based on the analysis of internal heat transfer and surface convective heat transfer of the road, the author set suitable boundary conditions of temperature field according to the actual situation of the road pavement and made quantitative research on the influence of factors such as the evaporating section burial depth and arrangement distance of the heat pipe on the pavement temperature field. The results show that the optimal arrangement distance of the heat pipes is 300-500mm, and when the heat pipe distance is 500mm, the temperatures of the road surface increased slowly, the isotherm of 0°C will no longer continue, and the snow-melting time would become longer; heat pipe burial depth should be determined according to the road structure, the shallower the burial depth the better the snow-melting effect..

Evaluation of System Reliability and Optimal Design: The Case of Consecutive-&lt;i&gt;k&lt;/i&gt; Systems

Evaluation of System Reliability and Optimal Design: The Case of Consecutive-&lt;i&gt;k&lt;/i&gt; Systems

Reliability Evaluation and Analysis of FPGA-Based Neural Network Acceleration System

Prior works typically conducted the fault analysis of neural network accelerator computing arrays with simulation and focused on the prediction accuracy loss of the neural network models. There is still a lack of systematic fault analysis of the neural network acceleration system that considers both the accuracy degradation and system exceptions, such as system stall and running overtime. To that end, we implemented a representative neural network accelerator and corresponding fault injection modules on a Xilinx ARM-FPGA platform and evaluated the reliability of the system under different fault injection rates when a series of typical neural network models are deployed on the neural network acceleration system. The entire fault injection and reliability evaluation system is open-sourced on GitHub. With comprehensive experiments on the system, we identify the system exceptions based on the various abnormal behaviors of the FPGA-based neural network acceleration system and analyze the underlying reasons. Particularly, we find that the probability of the system exceptions dominates the reliability of the system. The faults also incur accuracy degradation of the neural network models, but the influence depends on the applications of the models and can vary greatly. In addition, we also evaluated the use of conventional triple modular redundancy (TMR) and demonstrated the challenge of TMR with both experiments and analytical models, which may shed light on the reliability design of the FPGA-based neural network acceleration system.

Scale parameter assessment based on pivot quantity for reliability analysis of a complex system

Reliability assessment is the key to the reliability design of a complex system. This paper takes the lithium battery pole slice separators (LBPSS) as the research object and selects the superposition Weibull model as the initial model. Based on the graphic method, a pivot quantity is introduced to estimate the range of scale parameters. A genetic algorithm is used to optimize parameters in a given parameter interval. Compared with other models and techniques, using the superposition Weibull model, pivot quantity, and graphic method to evaluate the reliability of LBPSS is more reasonable, provides a reference for improving the reliability and maintainability of LBPSS, and lays a theoretical foundation for analyzing the failure mode and failure mechanism of a complex system.