Traditional System Reliability Research Articles

A Bayesian design method for monopropellant engine system reliability qualification test plan

During the development of systems, traditional System Reliability Qualification Testing (SRQT) is typically utilized to assess whether they meet predefined reliability standards. However, this approach often demands substantial sample sizes and prolonged test durations, rendering it impractical for costly, highly reliable systems with limited sample sizes. Additionally, the extended test duration may not align with practical time-to-market pressures or budget constraints. To overcome these challenges, the study integrates subsystem data into the monopropellant engine system RQT plan's design. By leveraging Monte-Carlo simulation, subsystem data is modeled to create a system parameter distribution, enabling the formulation of SRQT plans based on posterior risks. An example of a monopropellant liquid rocket engine system is provided to demonstrate the advantages and applications of the proposed methodology.

Research on Improvement of Data Synchronization Method for Distribution Network Differential Protection Based on 5G

To enhance the reliability of traditional current protection systems in the evolving landscape of future power grids, this paper proposes a differential protection data synchronization approach that leverages satellite and 5G network timing. This method addresses the risk of inadvertent operation due to 5G communication jitter. It employs the BeiDou satellite system for synchronizing the timing of 5G base stations and terminals. This synchronization extends to the 5G terminal and the protection device, thereby mitigating time deviations caused by air interface delays and reducing the 5G network’s end-to-end delay by approximately 4 ms on average. A simulation was conducted to assess the reliability and effectiveness of the proposed method under normal satellite signal conditions. The results indicated a significant improvement in protection accuracy, with the method reducing the protection activation time by approximately 5–7 ms compared to the 5G timing data synchronization method. This advancement offers technical support for the safe and stable operation of new power systems.

Use of Bio-Epoxies and Their Effect on the Performance of Polymer Composites: A Critical Review.

This study comprehensively examines recent developments in bio-epoxy resins and their applications in composites. Despite the reliability of traditional epoxy systems, the increasing demand for sustainability has driven researchers and industries to explore new bio-based alternatives. Additionally, natural fibers have the potential to serve as environmentally friendly substitutes for synthetic ones, contributing to the production of lightweight and biodegradable composites. Enhancing the mechanical properties of these bio-composites also involves improving the compatibility between the matrix and fibers. The use of bio-epoxy resins facilitates better adhesion of natural composite constituents, addressing sustainability and environmental concerns. The principles and methods proposed for both available commercial and especially non-commercial bio-epoxy solutions are investigated, with a focus on promising renewable sources like wood, food waste, and vegetable oils. Bio-epoxy systems with a minimum bio-content of 20% are analyzed from a thermomechanical perspective. This review also discusses the effect of incorporating synthetic and natural fibers into bio-epoxy resins both on their own and in hybrid form. A comparative analysis is conducted against traditional epoxy-based references, with the aim of emphasizing viable alternatives. The focus is on addressing their benefits and challenges in applications fields such as aviation and the automotive industry.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Software-hardware embedded system reliability modeling with failure dependency and masked data

Traditional system reliability models often ignore failure dependency between subsystems and the existence of system failure masked data, thereby they can’t accurately reflect the reliability modeling analysis of the whole system. This paper investigates an embedded system that comprises software subsystems and hardware subsystems. In order to more accurately assess the embedded system’s reliability, we proposed a reliability superposition model of the software-hardware system with masked data and failure dependency. In the model, the influence of masked failure data and failure dependency between the hardware subsystem and software subsystem is considered in system reliability evaluation, and the influence of fault dependence is solved by Copula function. Estimating the parameters of this model is a challenging task due to the complexity of the parameters. With regards to this, we proposed an immune particle swarm optimization algorithm with enhanced learning ability (IPSO-ELA), which is used to calculate the parameter’s estimation. Additionally, we investigated the impact of varying degrees of failure dependence on system reliability. Finally, the numerical experiment shown that the proposed model, which considers failure dependency among subsystems, outperforms other reliability models that do not. It can be seen from the experimental fitting results and the reliability trend charts that the system reliability when failure dependence is considered is higher than isn’t considered, which is more realistic.

A Secure Lossless Redundancy Elimination Scheme With Semantic Awareness for Cloud-Assisted Health Systems

Due to the limited scalability and reliability of traditional storage systems, more and more medical data are stored in cloud. However, the cost of cloud-assisted health systems is skyrocketing. How to improve storage utilization and save resource cost for health systems has become urgent problems. Therefore, this study proposes a secure lossless redundancy elimination scheme with semantic awareness, called SLRE, to decrease storage overhead and improve the overall system performance. Compared with existing studies, when the locality of data is almost nonexistent or poor, SLRE can eliminate redundant data well by exploiting semantic properties of similarity and entropy rather than relying on the locality. Then, fine-grained post-deduplication delta compression based on proxy similarity discovers more similar blocks and improves storage efficiency. In addition, filtered Lempel–Ziv compression based on proxy entropy filters data with low redundancy to improve the overall performance. Importantly, in order to ensure the security of medical data after deredundancy, different auditing strategies for medical data with diverse characteristics are proposed to guarantee security and improve the auditing efficiency. The experimental results show that compared with the state-of-the-art methods, the overall compression ratio of SLRE is improved, and the system throughput and auditing efficiency are also improved.

Time-domain Reliability Estimation of Gear Train considering Failure-dependence

A gear tooth or a gear is usually considered as an independent component in the traditional reliability research. However, gear trains are significantly different from the traditional series systems. Time-dependent configuration of the traditional series systems is fixed. But the meshing tooth-pairs of gear trains are changing all the time during operation. The failures of meshing tooth-pairs are dependent statistically under the common load action. In the paper, the reliability analysis considering time-domain meshing relation of tooth-pairs is studied and meshing relation equations are presented. Then, reliability analysis method of gear trains is developed based on the Monte Carlo simulation with dependent failure and time domain meshing relation. The differences of reliability results between time domain reliability analysis method and traditional series system reliability methods are illustrated by several examples.

Multikernel: Operating system solution to generalized functional safety

With the trend of digitalization, intelligence, and networking sweeping the world, functional safety and cyber security are increasingly intertwined and overlapped, evolving into the issue of generalized functional safety. Traditional system reliability technology and network defense technology cannot provide quantifiable design implementation theories and methods. As the cornerstone of software systems, operating systems in particular are in need of efficient safety assurance. The DHR architecture is a mature and comprehensive solution, and it is necessary to implement an OS-level DHR architecture, for which the multi-kernel operating system is a good carrier. The multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy, heterogeneity, and dynamism on the kernel, so it has the generalized robustness of the DHR architecture. This article analyzes the significance and requirements of OS-level DHR architecture, and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

Large-Area Saline-Alkali-Tolerant Rice Growth Environment Monitoring System Based on LoRa+UAV

A wireless sensor network (WSN) monitoring system for saline-alkali-tolerant rice based on long-range + unmanned aerial vehicle (LoRa + UAV) has been proposed in this paper to meet the requirements of difficult deployment, low scalability, and poor network reliability of traditional farmland environmental monitoring systems. The system automatically forms a double-layer star topology network through the terminal node and central node (intranet), central node, and UAV mobile node (extranet). Data transmission is carried out through LoRa technology, routing planning is carried out for UAV mobile receiving nodes at different flight altitudes, and an early warning mechanism for UAV energy is added. It effectively prevents the UAV from destroying the communication capability of the external network due to energy exhaustion during operation. The system transmits information remotely to the server of the management center through a 5G network, which analyzes and processes the data and publishes it on the Web. The system has been tested in the field, and the reliable communication range of the node is 1∼4 km under the saline-alkali-tolerant rice environment, and the service life of the terminal node can reach more than 3 months. During continuous operation, the maximum packet loss rate of the internal and external integrated networks is 1.45%. The system can stably and real-time monitor saline-alkali-tolerant rice environmental information, and the error rate is less than 10%, which can meet the performance requirements of the saline-alkali-tolerant rice environmental monitoring systems, such as low power consumption, wide coverage, stability, and reliability, and has a good engineering significance.

Wireless Sensor Network Physical Layer Authentication Technology Based on Dynamic Prediction of Electromagnetic Fingerprints

The physical layer characteristics of the signal, such as electromagnetic fingerprints, will change with the time-varying channel’s variation under the influence of various factors, so that the reliability of traditional electromagnetic fingerprint authentication systems that don’t consider the dynamic changes of electromagnetic fingerprints would be reduced. In this paper, we explore and design a wireless sensor network physical layer authentication mechanism based on electromagnetic fingerprint dynamic prediction to improve the reliability of the authentication system. This system uses the LSTM network to learn and predict the dynamic of electromagnetic fingerprints affected by wireless channels and combines the unique electromagnetic fingerprint differences of legal transmitters with the channel corresponding changes to transceivers to improve the difference between the electromagnetic fingerprints from legitimate communication and the attack link, further improving the accuracy of electromagnetic fingerprint authentication. In addition, the LSTM network can also find the abnormal data and learn through continuous adaptive training to improve the detection probability.

An efficient authenticated key agreement scheme supporting privacy-preservation for smart grid communication

As a branch of the next generation grid, smart grid(SG) combines information and communication technology with traditional grid architecture, which can greatly improve the efficiency and reliability of traditional power systems. However, the tampering of power data in smart grid communications may lead to unclear decisions related to power demand management. So, a secure authentication method which achieves SG device validation in different regions is a basic requirement. In the paper, we put forth a secure authentication key agreement(AKA) scheme supporting privacy-preservation in SG communications. This scheme allows SG devices and service provider(SP) to establish secret session key through mutual authentication. With the session key, the sensitive information can be exchanged securely. Through rigorous security and performance analysis, our proposed solution has stronger security functions and higher computational efficiency than related solutions in the smart grid.

Stochastic analysis of k-out-of-n: G type of repairable system in combination of subsystems with controllers and multi repair approach

This paper describes the investigation of different reliability measures of a complex system consisting of two subsystems with controllers in a series configuration, which is a useful opportunity for specific design problems. Subsystem-1 consisting n units functioning under the policy k-out-of-n: G; policy, and subsystem-2 has m units and operating under r-out-of-m: G; policy. The system failure rates of both subsystems are constant and assumed to obey an exponential distribution; two types of distribution are allowed to repair: general distribution and Gumbel-Hougaard family copula distribution. The system's partially failed states/ completely failed states are repaired using General/ copula distribution. After repair, the units in both the subsystems are good as new. The controller control both subsystems and the failure of controllers brings the subsystem in the complete failed state. The operator may fail the system deliberately if not satisfied with the organization. The system is analyzed employing the supplementary variable technique, and Laplace transforms implications and traditional system reliability measures, such as the system's availability, system reliability, and profit analysis, have been computed for particular values of failure and repair parameters.

A convolutional neural network-based approach to composite power system reliability evaluation

This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development.

Feasibility Analysis of Reliability Analysis Methods Applying in Air Traffic Control System

Air traffic control system plays a vital role of air traffic management system, of which reliability determines the service quality and supply level of air transportation. The structure of air traffic control system is considered, and an expression of reliability analysis is applied. A mount of methods including traditional reliability analysis and network system reliability analysis are introduced to demonstrate whether they are feasibility for reliability analysis of air traffic control system. This work can be used as reference and basic for air traffic control system, which is meaningful for ATM safety operation, system selection and response repair.

Component-based system reliability subject to positive and negative correlation

Traditional system reliability modeling methods assume statistical independence of component failures, while past research emphasizes methods to characterize positive correlation and dependence. However, negative dependence and correlation are also important for system reliability and resilience modeling. This paper develops a method to assess the reliability of a system subject to arbitrary combinations of positive and negative correlation between the failures of its components. The method enables derivation of expressions composed of a vector of unequal component reliabilities and unequal pairwise component correlations. The method is illustrated through a sequence of examples, which conduct sensitivity analysis on correlation parameters of the model as well as quantify the impact of component correlations on system reliability. Coverage experiments formally assess the flexibility and scalability of the approach.

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

Driving circuitry of a full-frame area array charge-coupled device (CCD) supporting multiple output modes and electronic image motion compensation

In order to design a charge-coupled device (CCD) driving circuit with a high frame rate and a large area array which can support the electronic image motion compensation function, first a basic driving circuit of the large-array CCD is reported in this paper. Next, electronic image motion compensation is realized by adding an image motion compensation timing generator and a main timing generator, Saa8103. The internal design structure of the image motion compensation generator is provided and is characterized as follows. The data are first sent by the system controller, the main timing generator and the image motion compensation timing generator. The generator, the vertical driver, the horizontal driver and the full-frame charge-coupled device chip are then composed. During the exposure period, the image motion compensation transfer clock is generated by the image motion compensation timing generator, and the charge-coupled device charge packet is driven to track the image movement after being amplified by the vertical and horizontal drivers to carry out electronic image motion compensation in the vertical direction. During the output period, the vertical and horizontal rotations generated by the main timing generator are carried out by the image motion compensation timing generator. The shift clock is transmitted to the vertical and horizontal drivers to drive the charge-coupled device to produce the output. The phase relationship of the four corners of the charge-coupled device is independently controlled by the image motion compensation timing generator, which supports not only the electronic image motion compensation but also the flexible selection of the output channel number and output mode. A field-programable gate array (FPGA)/complex programable logic device (CPLD) has been selected to be the timing generator of image motion compensation, and timing simulation is carried out. Lastly, the design of the driving circuit was verified by an indoor image motion compensation experiment, and a good compensation effect has been achieved. In this system, the charge-coupled device adopts a separate driving technology, and the charge transfer direction of each block can be established. Consequently, it is convenient to select the number of output channels and the output mode, which makes the camera suitable for different occasions, supports the charge-coupled device in terms of the output and electronic image motion compensation in various ways, and is capable of generating full-frame images. In order to meet the different requirements regarding the charge-coupled device frame frequency in different contexts, this proposed design overcomes the disadvantages of the complex structure and low reliability of traditional optical mechanical image motion compensation systems.

Reliability Evaluation for Aviation Electric Power System in Consideration of Uncertainty

With the rapid development of more electric aircraft (MEA) in recent years, the aviation electric power system (AEPS) has played an increasingly important role in safe flight. However, as a highly reliable system, because of its complicated flight conditions and architecture, it often proves significant uncertainty in its failure occurrence and consequence. Thus, more and more stakeholders, e.g., passengers, aviation administration departments, are dissatisfied with the traditional system reliability analysis, in which failure uncertainty is not considered and system reliability probability is a constant value at a given time. To overcome this disadvantage, we propose a new methodology in the AEPS reliability evaluation. First, we perform a random sampling from the probability distributions of components’ failure rates and compute the system reliability at each sample point; after that, we use variance, confidence interval, and probability density function to quantify the uncertainty of system reliability. Finally, we perform the new method on a series–parallel system and an AEPS. The results show that the power supply reliability of AEPS is uncertain and the uncertainty varies with system time even though the uncertainty of each component’s failure is quite small; therefore it is necessary to quantify system uncertainty for safer flight, and our proposed method could be an effective way to accomplish this quantization task.

Bounds approximation of limit‐state surface based on active learning Kriging model with truncated candidate region for random‐interval hybrid reliability analysis

SummaryThis article reports a brand‐new methodology based on active learning Kriging model for hybrid reliability analysis (HRA) with both random and interval variables. Unlike probabilistic reliability analysis, the limit state surface (LSS) of HRA is projected into a banded region in the domain of random variables. Only approximating the bounds of the banded region is able to meet the accuracy requirement of HRA. In the proposed methodology, the HRA problem is innovatively transformed into a traditional system reliability analysis (SRA) problem with numerous failure modes. And then a basic idea from the field of SRA is borrowed into HRA, and the so‐called truncated candidate region (TCR) for HRA is proposed. In each iteration, the negligible region which probably does not influence the bounds estimation of failure probability is truncated from the original candidate region, and the optimal training point is chosen from the TCR. After several iterations, the TCR will converge to the true ideal candidate region, that is, the candidate region without the inner part of LSS, and the added training points will be driven to the region around the bounds of LSS. The performance of the proposed method is compared with relevant methods by five case studies.

Badanie modelu niezawodności ruchów sekwencyjnych oraz propozycja rozwiązania

The missions of weapon systems are becoming increasingly complex. Thus, more mechanism motions than one are required to complete one mission. Under such conditions, a sort of mission has emerged, that needs a few mechanism motions to be executed in sequence. This means that the mission is not completed until all the motions have been executed successfully in strict sequence. This sequence motion system can be considered as a traditional series system with the motions treated as subsystems. Then, the system reliability can be analyzed with the traditional series system reliability method. However, this method cannot fully reflect the characteristics of a sequence. In this work, a reliability model of sequence motions and its solving idea are proposed. In this reliability model, the influence factors of each motion are included. Particularly, the performance function of the former motion is regarded as just one of the influence factors of the next motion, which is the most significant feature for the sequence motion system. Afterward, a solving idea with characteristics of a gradually shrinking sample space is proposed based on Monte-Carlo simulation. Finally, the reliability model of sequence motions and its solving idea are illustrated with a case study on the automatic chain shell magazine sequence motions of a self-propelled artillery.