Search

Cloud and edge computing are necessary elements in the modern distributed network. To reflect the capacity variances of the system, stochastic capacities and costs of components (arcs and nodes) are considered such that stochastic capacity distributed networks (SCDNs) can be formed in the paper. To learn the capability of the SCDN for system monitoring and management, system reliability can be utilized which is defined as the probability of satisfying demands (data) under a budget for an SCDN. An exact-system reliability algorithm for system reliability is derived in advance for prudent management and decision-making. For immediate control and management, a deep neural network (DNN) architecture is proposed to create a prediction model for system reliability. Enough data points about SCDN information are generated and transformed into an appropriate format for training the prediction model. Note that the labels (system reliability) for all the data points would be calculated using the exact-system reliability algorithm. Necessary hyperparameters for determining the DNN of system reliability of the SCDN are also suggested. The model can be used for the future management of the distributed network. [ABSTRACT FROM AUTHOR]

Reliability demonstration test (RDT) is the test to verify that the reliability of the product meets the reliability requirements during product development and is usually required to be performed to determine whether it can be accepted. It is essential for complex systems with higher costs. However, the traditional test plans such as the plan in the current RDT standard GJB‐899A have the problem of too long test time or too high risk. In addition, the test data of the complex system itself is usually less, but the test data of the subsystem is easier to obtain. In this paper, the research object is the system, and the main research content is how to use the reliability growth data in the development stage of the subsystem to design the system RDT. First, based on the reliability growth model, the test data in the development stage of the subsystems are modeled to obtain the reliability of each subsystem. The Bayesian theory is used to obtain the lifetime distribution of the system. Then, according to the definition of the two types of risks in the RDT, the life distribution of the product is integrated into the two types of risks, and the RDT plans of the system are designed. Finally, a simulation case is used to verify our RDT design method, and the results show that compared with the traditional plan, our RDT plan designed has a shorter test time and lower test risk. [ABSTRACT FROM AUTHOR]

In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected degradation can result in failures with significant consequences. For instance, a minor crack in an oil pipeline might go unnoticed, eventually leading to a major leak, environmental harm, and costly cleanup efforts. Similarly, in the nuclear industry, undetected degradation in reactor cooling systems could cause overheating and potentially catastrophic failure. This paper focuses on reliability modeling for systems experiencing degradation, accounting for time delays associated with undetected degraded states, self-repair mechanisms, and varying operating environments. The paper presents a reliability model for degraded, time-dependent systems, incorporating various aspects of degradation. It first discusses the model assumptions and formulation, followed by numerical results obtained from system modeling using the developed program. Various scenarios are illustrated, incorporating time delays and different parameter values. Through computational analysis of these complex systems, we observe that the probability of the system being in the undetected degraded state tends to stabilize shortly after the initial degradation begins. The model is valuable for predicting and establishing an upper bound on the probability of the undetected, degraded state and the system's overall reliability. Finally, the paper outlines potential avenues for future research. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid technique for optimizing Combined Cooling, Heating, and Power (CCHP) systems integrating a diverse set of components including geothermal heat pumps, gas turbines, absorption chillers/heaters, and photovoltaic/thermal collectors, along with storage units like batteries and water tanks. The innovation lies in combining the Capuchin Search Algorithm (CapSA) with the Wild Horse Optimizer (WHO) to concurrently optimize primary energy savings, cost reductions, and carbon dioxide emissions. Parametric distributions are used in probabilistic strategies to resolve uncertainty in solar radiation and building load. CapSA optimizes the CCHP system’s design, while WHO enhances its performance. The effectiveness of the proposed strategy is demonstrated through implementation in MATLAB, where it achieves an average ACSR (Average Comprehensive Saving Rate) enhancement of 5.48%. This numerical result highlights a substantial improvement over existing methodologies, showcasing the superior performance of the hybrid optimization approach in enhancing energy efficiency and environmental sustainability of CCHP systems. [ABSTRACT FROM AUTHOR]

The convergence of heterogeneous wireless sensor networks provides many benefits, including increased coverage, flexible load balancing capabilities, more efficient use of network resources, and the provision of additional data by different types of sensors, thus leading to improved customer service based on more complete information. However, despite these advances, the challenge of ensuring reliability and survivability remains due to low-cost sensor requirements and the inherent unreliability of the wireless environment. Integrating different sensor networks and unifying protocols naturally leads to the creation of a network with multiple sinks, necessitating the exploration of new approaches to rational reliability assurance. The failure of some sensors does not necessarily lead to a shutdown of the network, since other sensors can duplicate information and deliver data to sinks via an increased number of alternative routes. In this paper, the reliability indicator is defined as the probability that sinks can collect data from a given number of sensors. In this context, a dedicated reliability metric is introduced and examined for its effectiveness. This metric is computed using an algorithm rooted in the modified factoring method. Furthermore, we introduce a heuristic algorithm designed for optimal sink placement in wireless sensor networks to achieve the highest level of network reliability. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a reliability evaluation method of a distribution network with electric vehicles (EVs) and distributed generations (DGs) based on network equivalence and sequential Monte Carlo. Unlike traditional studies, the proposed method not only can effectively simplify the complexity structure of the distribution network with EVs and DGs, but also can establish a multidimensional reliability evaluation system for the distribution network with EVs and DGs. Firstly, the related models of DGs and EVs are established respectively in our study. Based on the minimum path method, the impact of each component fault on each load point is analyzed, and the failure impact analysis (FIA) table is constructed. Then, a simplified method for the structure of the distribution network with EVs and DGs based on network equivalence is proposed and validated. Moreover, the reliability evaluation method of the distribution network with EVs and DGs is given based on the sequential Monte Carlo. Finally, an illustrative example of the improved IEEE RBTS BUS6 F4 feeder system, as well as multiple dimensions discussions, are elaborated in detail to demonstrate the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems. Experts describe the degree of failure of the event in the form of interval numbers. The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node. Then, the Hyper-ellipsoidal model (HM) constrains the initial failure probability interval and constructs a HE-BN for the system. A reliability analysis method is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure. The failure probability of the system is further calculated and the key components that affect the system's reliability are identified. The proposed method accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses. The feasibility and accuracy of the method are further verified by conducting case studies. [ABSTRACT FROM AUTHOR]

Strong earthquake disasters can cause noticeable damage correlations among regional buildings with similar mechanical properties, termed structure‐to‐structure seismic damage correlation, which has a pronounced impact on the regional seismic risk assessment and therefore needs to be properly quantified. This study first introduces a time‐domain analytical and equivalent frequency‐domain analysis based on random vibration theory for calculating structure‐to‐structure seismic damage correlation coefficients. Subsequently, by employing the spatially consistent white noise excitation and the spatially varying white noise excitation with the Luco–Wong coherence function, the structural filtering effect and the ground motion spatial correlation effect are progressively incorporated, and an analytical interstructural damage correlation model incorporating structural dynamic properties and spatial distance is derived. Comparations with Monte Carlo simulations and existing empirical models demonstrate that the proposed analytical model possesses a clear physical basis and high reliability. Finally, a case study was conducted on a district having 29,461 buildings in Shanghai, China to illustrate the influence of interstructural damage correlation on the regional seismic risk. Results show that disregarding the interstructural seismic damage correlation can lead to underestimation of overall loss uncertainty. [ABSTRACT FROM AUTHOR]

Rural areas with weak distribution networks often suffer from a lack of energy supply reliability, which makes it difficult to widely implement multi-energy complementary systems connected to large-scale photovoltaic. Energy storage systems, as a link to the integration of new energy systems, can effectively improve the reliability of energy supply in such areas. To this end, a cooperative and optimal configuration strategy for rural multi-energy complementary system (RMES) energy storage for weak distribution networks is proposed. Firstly, the RMES architecture including biomass waste energy conversion system is clarified. Secondly, an energy storage configuration evaluation criterion is proposed using the cost of supply deficiency loss of the system as a judgment basis. Finally, the RMES multi-energy storage cooptimal allocation model is established to take into account the total life cycle cost of energy storage. The model is decoupled and transformed into a two-layer optimization model with two stages of planning operation, and solved by a hybrid strategy combining particle swarm algorithm and Gurobi solver. The analysis of the algorithm shows that the proposed energy storage configuration evaluation criteria effectively motivate RMES to deploy energy storage. In addition, the developed multi-variate energy storage configuration model enables RMES to show better economic and environmental benefits, and is superior in improving system reliability.

To consider the complex soil-structure interaction in a pile-slope system, it is necessary to analyze the performance of pile-slope systems based on a three-dimensional (3D) numerical model. Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability. In this paper, an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes, where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure, respectively. The versatility of the suggested method is illustrated in detail with an example. For the example examined in this paper, it is found that the pile failure can significantly contribute to system failure, and the reinforcement ratio can effectively reduce the probability of pile failure. There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio. The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope. There exists an optimal location and an optimal length for the stabilizing piles.

Abstract This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management.

This paper assesses the reliability of landfill veneer cover systems against direct-sliding failure (DSF) and uplifted-floating failure (UFF) modes using the second-order reliability method (SORM). Variability in stability number due to cohesion, interface cohesion, friction angle, horizontal seismic acceleration coefficient, and phase changes are considered. Formulations for the first-order reliability method (FORM), SORM, and Monte Carlo simulations (MCS) are presented, with emphasis on the precision of SORM over FORM. A novel framework for system reliability–based design optimization (SRBDO) is introduced, addressing interdependencies between DSF and UFF modes. The results highlight the significant influence of the lowest component reliability index on system reliability. For the given design conditions, the system reliability index is obtained as 1.55, significantly influenced by the component reliability indices of 1.55 and 9.29. In addition, this study provides a comparative analysis of reliability indices obtained using SORM for static, pseudostatic (PS), and pseudodynamic (PD) methods. Moreover, system reliability–based design charts are generated to estimate allowable cover soil thickness to ensure concurrent reliability in DSF and UFF modes. It is noted that an increase in the value of the horizontal seismic acceleration coefficient from 0 to 0.30 results in a 77.27% increase in the allowable thickness of cover soil. Practical Applications: The computational framework of Monte Carlo simulations, first-order reliability method, and second-order reliability method proposed in this study can be utilized to develop an optimum and reliability-based design of municipal solid waste landfill slopes, geosynthetic-reinforced soil slopes, retaining walls, embankments, and other geotechnical structures. Considering the variability associated with the design parameters enables the practitioners to make well-informed decisions regarding the selection and specification of materials. The system reliability framework presented in this study allows for a more comprehensive understanding of the interdependency between the different modes of failure of a system. The insights of this study can be utilized to develop more robust designs of structures equipped to withstand various failure modes. The comparison of reliability indices obtained using the static, pseudostatic, and pseudodynamic methods enables the practitioners to adapt design approaches to varying environmental and loading conditions, ensuring the long-term stability of the structures. The generation of system reliability–based design charts offers practical tools for estimating the allowable thickness of cover soil while ensuring reliability against both DSF and UFF modes simultaneously. Moreover, the findings of this research contribute to the establishment of global design standards for ensuring seismic stability in veneer cover systems. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results. [ABSTRACT FROM AUTHOR]

The aim of this research is to devise a continuous simulation model for predicting ship crane failures to increase their reliability and reduce unplanned downtime during cargo loading and unloading operations. To predict the condition of the hydraulic system, a database from the GALIOT software package was used for carrying out maintenance on cranes at m/v "O" over a period of 120,000 working hours. In the research, fault tree analysis (FTA) was used to identify causal relationships between system failures and basic events, while the Markov mathematical model was used to model the system state and predict transitions between different failure states. A system dynamics simulation model was developed to simulate the behavior of a system using POWERSIM PowerSim Constructor 2.5.d (4002), and regression analysis was performed to analyze the simulation results and understand the relationships between dependent and independent variables. The results show that a model for predicting failures in the hydraulic motors and pumps of ship cranes was developed, and the Markov model makes it possible to estimate the frequency of transitions between states under the condition that the sum of reliability equals one. The simulation model shows high reliability of the cranes and a constant frequency of failures throughout the 120,000 operating hours, while the regression analysis confirms the validity of the simulation model and shows a strong correlation between the analyzed variables. These models are used to improve the planning of ship crane maintenance, reduce unplanned downtime, and predict and promptly detect failures, which overall minimizes maintenance costs and failures. [ABSTRACT FROM AUTHOR]

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a 'detection' inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Products are often offered with warranties, which have become a common marketing strategy intended to indicate that product quality is guaranteed by the firm. Warranties can attract consumer purchases, but they also incur warranty costs, especially when firms falsely claim that their products are high quality; therefore, the determination of an appropriate warranty policy is of particular importance. This study considers a two‐dimensional warranty for complex repairable products, such as a vehicle, a lathe, or industrial machinery, with consideration of (1) burn‐in (run‐in), which is a supervised accelerating testing process performed before product launch to prevent defective products from being sold on the market, and (2) periodic preventive maintenance, which is performed during the usage period to prevent the occurrence of unexpected failures under normal usage conditions. The optimal burn‐in (run‐in) time and preventive maintenance intervals are determined with the aim of minimizing warranty costs for firms. The results of the study show that given a higher repair cost and a greater infant mortality failure rate, more frequent preventive maintenance is preferable, and the repair cost has the largest effect on the total warranty cost for the firm. [ABSTRACT FROM AUTHOR]

Distributed real time system has developed into an outstanding computing platform for parallel, high-efficiency applications. A real time system is a kind of planning where tasks must be completed with accurate results within a predetermined amount of time. It is well known that obtaining an optimal assignment of tasks for more than three processors is an NP-hard problem. This article examines the issue of assigning tasks to processors in heterogeneous distributed systems with a view to reduce cost and response time of the system while maximizing system reliability. The proposed method is carried out in two phases, Phase Ⅰ provides a hybrid HPSOGAK, that is an integration of particle swarm optimization (PSO), genetic algorithm (GA), and k -means technique while Phase Ⅱ is based on GA. By updating cluster centroids with PSO and GA and then using them like initial centroids for the k -means algorithm to generate the task-clusters, HPSOGAK produces 'm' clusters of 'r' tasks, and then their assignment onto the appropriate processor is done by using GA. The performance of GA has been improved in this article by introducing new crossover and mutation operators, and the functionality of traditional PSO has been enhanced by combining it with GA. Numerous examples from various research articles are employed to evaluate the efficiency of the proposed technique, and the numerical results are contrasted with well-known existing models. The proposed method enhances PIR values by 22.64%, efficiency by 6.93%, and response times by 23.8 on average. The experimental results demonstrate that the suggested method outperforms all comparable approaches, leading to the achievement of superior results. The developed mechanism is acceptable for an erratic number of tasks and processors with both types of fuzzy and crisp time. [ABSTRACT FROM AUTHOR]

Bogie is a pivotal system and plays a critical part in the safety and reliability management of high-speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to low efficiency. In order to overcome these drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d -MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d -MC model is established to remove unnecessary d -MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to test the practicability of the method proposed in this article; it is found that this method outperforms a newly developed method in solving multi-state reliability problems. [ABSTRACT FROM AUTHOR]

The parameters of gravity dams and foundation materials objectively exhibit spatial variability due to environmental and load influences, which significantly affect the safety status of dam structures. Therefore, a safety risk analysis method for a gravity dam–foundation system based on random field theory is proposed in this paper. Spatial variabilities in materials are particularly considered by using the finite element method. Then, composite response surface equations for the performance function (PF) of strength and stability failure are established, and then, the system failure risk is obtained using the Monte Carlo method. The proposed method solves the problem wherein the effect of spatial variability on failure risk cannot be reflected accurately by the performance function of multi-element sliding paths, and the difficulties in solving the failure risk of the series–parallel system due to multiple failure paths and their complex correlations. The application of a gravity dam shows that the developed method overcomes the disadvantages of the traditional method, such as the homogenization of the spatially random characteristics of parameters and the overestimation of failure risk in the system due to large variance estimation. [ABSTRACT FROM AUTHOR]

The system reliability analysis of complex engineering structures remains a challenge in the field of reliability. In this work, a high-order moment method using generalized factorized dimensional reduction method (GF-DRM) and iterative maximum entropy method (IMEM) is proposed for structural system reliability assessment. Firstly, the performance functions are described uniformly by equivalent description methods for various system reliability problems. Secondly, a novel point estimation method (PEM) based on GF-DRM is proposed for the high-order moments estimation of the equivalent performance function. Thirdly, the system failure probability of the structures is accurately determined by using the IMEM, which can automatically determine the order of moments. Finally, four examples, including numerical and engineering cases, are investigated to demonstrate the accuracy and efficiency of the proposed method, in which results obtained from the proposed method are compared with some other existing moment methods and Monte Carlo simulation (MCS) method. The results of the examples show the proposed method has fairly high accuracy and efficiency for structural highorder moments estimation and system reliability analysis. [ABSTRACT FROM AUTHOR]

Based on stepped sliding surfaces, back edge tensile cracks divide the slope bodies into upper (region B) and lower (region A) parts. How region B affects region A, and the instability sequence and interaction relationship between regions A and B need to be further clarified. Based on this, an improved method to analyze the interaction relationship of the two regions is proposed based on the coordination relation of their accelerations. Based on system reliability theory and the possible instability sequence of regions A and B, a slope stability analysis system composed of four failure cases is constructed. Then, the limit equilibrium method and the Monte Carlo method are adopted to analyze the failure probability and dominant failure mode of the slope stability analysis system. In addition, the accuracy of the improved method is verified through comparison with existing theoretical research and discontinuous deformation analysis numerical simulation methods. The results show that with an increase in the fracture connectivity ratios (kA and kB) of the two regions, the slope stability shows a downward trend. In particular, region A is more prone to instability alone with an increase in kA. Region A is more prone to instability due to the interaction relationship because of region B with an increase in kB. In addition, with the increasing of weakening coefficients (Kc and Kφ), ratio of the joint spacing to the slope height (d/H), corresponding parameters of tensile cracks (L/H and β1) and groundwater level (Zw), region A is more prone to instability alone. In contrast, the seismic acceleration coefficients (kh and kv) aggravate the interaction relationship between the two regions and cause a sharp decrease in slope stability. Due to earthquake shaking of water, the seismic force effect on hydrous slopes is more significant with an increase in Zw. [ABSTRACT FROM AUTHOR]

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome. Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new probability distribution called the DUS Lindley distribution, created by applying the DUS transformation to the traditional Lindley distribution. The study provides an in-depth analysis of the distribution's statistical properties. These properties include a variety of statistical measures such as the probability density function, cumulative distribution function, failure rate, survival function, reverse hazard function, Mills ratio, mean residual life, mean past life, moments, conditional moments, characteristic function, order statistics, entropy measures, likelihood ratio test and Lorenz and Bonferroni curves. Parameter estimation is performed using several methods including weighted least squares, maximum likelihood estimation, Cramer-Von Mises estimation, least squares and Anderson-Darling estimation. The paper also explores the estimation of system reliability and evaluates the performance of maximum likelihood estimators through simulation studies across different sample sizes. Finally, the DUS Lindley distribution is applied to two real-world datasets, demonstrating a better fit than other well-known distributions.

Internet of Things (IoT) based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation. While performing this entire process, there is a high possibility for data corruption in the mid of transmission. On the other hand, the network performance is also affected due to various attacks. To address these issues, an efficient algorithm that jointly offers improved data storage and reliable routing is proposed. Initially, after the deployment of sensor nodes, the election of the storage node is achieved based on a fuzzy expert system. Improved Random Linear Network Coding (IRLNC) is used to create an encoded packet. This encoded packet from the source and neighboring nodes is transmitted to the storage node. Finally, to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector (DSDV) algorithm. Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics. Average residual energy, packet delivery ratio, compression ratio and storage time achieved for the proposed work are 8.8%, 0.92%, 0.82%, and 69 s. Based on this analysis, it is revealed that better data storage system and system reliability is attained using this proposed work.

A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.

In this paper, we look at the distributions of runs and scans in multistate Markov exchangeable sequences. The joint distributions of runs of several lengths under four types of enumeration schemes are analyzed. We evaluate the upper tail probabilities for ratchet scan statistics exactly. By utilizing the expansion of the generating functions, we propose effective computational tools for the derivation of probability functions. The results presented here provide approaches for the evaluation of the exact distributions of runs and scans in a wide class of practical problems. Finally, we discuss several applications and numerical examples to show how our theoretical results are applied to the investigation of runs and scans, as well as a parameter estimation problem. [ABSTRACT FROM AUTHOR]

In many production facilities, multiple components have to work together to share the overall workload on the entire system, leading to loading dependence and higher vulnerability to cascading failures. Additionally, overloading of one component can expedite the failures of others, exemplifying another form of loading dependence. In this study, we develop a system reliability analysis model for loading dependent systems considering overloads based on the Multi‐state CASCADE model. By incorporating a time variable, the tailor‐made model is able to characterize the duration of each generation in the cascading process, along with the cumulative time of the whole cascading process until the system collapse. A combination of analytical and simulation techniques is then employed to investigate how various potential influencing factors of loading dependence and cascading processes influence the system reliability. The results demonstrate that the effectiveness of proposed method in estimating the system reliability of the loading dependent system considering overloads. Such findings can improve the decision‐makings of reliability prediction, system design, and maintenance optimization, especially in scenarios involving the loading dependent with cascading failures. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual population collaborative harmony search algorithm with adaptive population size (DPCHS). Firstly, we propose a dual population algorithm framework for improving the algorithm global search capability. Within this framework, the guidance selection strategy and information interaction mechanism are integrated to strengthen the competition and cooperation among populations, and achieving a good balance between exploration and exploitation. A population state assessment method is designed to monitor population changes in real-time for enhancing population real-time self-regulation. Additionally, population size adjustment approach is designed to adopted to effectively streamline population resources and improve population quality. Comprehensive experiment results demonstrate that DPCHS effectively addresses system reliability-redundancy allocation problems with superior performance and robust convergence compared with other HS variants and algorithms from different categories. [ABSTRACT FROM AUTHOR]

Though road authorities have well-defined inspection protocols for evaluating bridge serviceability, gaps remain in understanding optimal timing and the impact of maintenance strategies on an individual bridge and its subsystems and, consequently, on the holistic performance of a road network. This paper proposes an innovative model based on system reliability principles that develops optimized bridge maintenance strategies aimed at enhancing the overall reliability of a bridge-dominated road network by considering the quantified impact of bridge component deterioration. Using the first-order second-moment method, we initially determine the reliability index for each bridge based on condition ratings derived from routine inspections as outlined in the bridge inspection manual. Advanced optimization algorithms then formulate the optimal maintenance strategies for the bridge structures. Finally, we gauge the system reliability of the network using Ditlevsen bounds approximation, considering the impact of maintenance actions on varied bridge structural components. The developed model was implemented on a typical bridge-dominated road network in Chongqing, China. The results demonstrate the model has the capacity to quantify the impact of diverse bridge maintenance strategies on the total road network, offering valuable insights for decision makers to design effective, efficient maintenance strategies aimed at extending the lifespan of bridge-dominated road networks. [ABSTRACT FROM AUTHOR]

Corrosion of steel girders in-service is essentially a random process, therefore, the corrosion depths have time- and spatiality-varying characteristics. However, only corrosion depth in single steel girder could be acquired by the measuring method available, and the joint distribution pattern of corrosion depths within the entire structural system is still unknown, which may result in unrealistic reliability estimates on the corroded bridge system. A steel-concrete composite bridge was selected as an object, the Gamma random process model was firstly established to simulate the growth of corrosion depth in each girder, and this simulation model was further verified by observations. Then, Kendall rank correlation coefficient was used to demonstrate the correlation ships of corrosion depths in steel girders, and the calculation equation was derived for the time-dependent resistance of corroded girders. Finally, based on the Copula function, the computation approach of failure probability was developed for bridge system, taking into consideration the spatial correlation of corrosion in girders, and the system failure probability with or without the corrosion correlation were compared. The results show: in the early operation stage, this kind of spatial correlation has few effects on structural performance, but in the middle and later periods, the system reliability will be remarkably affected when corrosion depths are highly correlated. The research proposed a method to quantify the corrosion correlation among steel girders, and developed the reliability assessment method with higher accuracy for steel-concrete composite bridge system, which could provide a reference for the time-dependent reliability evaluation of composite bridge systems. [ABSTRACT FROM AUTHOR]

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework's efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects. [ABSTRACT FROM AUTHOR]

When the load rating of a bridge is less than 1.0 for AASHTO HL-93 live load and state-specific legal trucks, the bridge is posted. Posting a bridge causes an inconvenience to the public and may result in trucks taking longer routes. Thus, this paper investigated the effects of field-derived distribution factor (DF) and impact factor (IM) from static and dynamic load tests using computer vision and deflection measurements, instead of AASHTO factors, on the bridge load rating and component as well as system reliability for posting avoidance. The reliability approach used Monte Carlo simulations to account for uncertainties in calculating the flexural strength limit state (Strength I). Both Flexural strength I and serviceability (Service III) limit states were investigated. The bridge's superstructure was studied in as-built, repaired, and damaged condition scenarios. For that, AASHTO HL-93, Florida legal trucks, and emergency vehicles were considered. In this regard, load rating distributions and component and system reliability indices were computed using both AASHTO and field-derived DF and IM. The increase in load rating and reliability was investigated. By using field-derived factors, a load rating increase of up to 17% was achieved. The component reliability also increased significantly. For example, the damaged case's component reliability increased by 1.0 when replacing DF and IM, which indicates a major reduction in the probability of failure. The increase in system reliability was most significant because using the field-derived DF increased the capacity contribution from other girders. A 2.12 increase in system reliability was achieved in the damaged condition when only substituting DF, providing even higher system reliability as a result of a reduction in the system probability of failure. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reliability analysis and evaluation of systems with multiple failure modes is extremely challenging. In this paper, a novel, efficient, and accurate system reliability method is proposed based on the second‐order approximation and the mixture important sampling (mixture‐IS) method. The proposed method does not require the construction of surrogate models and integrates the advantages of second‐order approximation and mixture‐IS strategies, which can greatly reduce the computational effort of estimating the system failure probability as well as ensuring the accuracy. The accuracy and efficiency of the proposed method are verified by several numerical and engineering problems, and comparisons are made against other existing reliability methods. The results indicate that the method can effectively reduce the number of limit state function calls and achieve high computational accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a novel model is proposed for the reliability analysis of systems subjected to dependent competing failure processes at random cycle times. The model takes into account the effect of random cycle time on degradation rate and hard failure threshold. Random cycle time is the time required for the system to complete a run. With the continuous operation of the system in a period of cycle time, the degradation rate of the system is accelerated, and its resistance to random shocks is decreased, so the degradation rate and hard failure threshold are supposed to vary with the number of cycles. When the overall degradation exceeds the soft failure threshold, soft failure is triggered. When the shock magnitude is larger than the hard failure threshold, hard failure occurs. Combined with the stress‐strength interference model, the reliability function of the system is derived. Finally, a Micro‐Electro‐Mechanical System is used to verify the validity of the proposed model. The validity of the model is demonstrated by sensitivity analysis. [ABSTRACT FROM AUTHOR]

A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability. [ABSTRACT FROM AUTHOR]

For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems. Experts describe the degree of failure of the event in the form of interval numbers. The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node. Then, the Hyper-ellipsoidal model (HM) constrains the initial failure probability interval and constructs a HE-BN for the system. A reliability analysis method is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure. The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified. The proposed method accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses. The feasibility and accuracy of the method are further verified by conducting case studies. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the insurance effect associated with distributed energy resources (DERs) within the context of system reliability. Drawing from the field of energy economics, we investigate the relationship between the failure risk of DER systems and the investment decisions made by consumers. By developing the certainty equivalent based on expected utility, we consider the choices made by both risk-neutral and risk-averse individuals. Our findings highlight the significance of risk aversion and system reliability in determining optimal investment levels in renewable DERs. This research contributes to the understanding of factors influencing investment behavior in DERs systems, offering valuable insights for policymakers and energy practitioners seeking to promote sustainable energy transitions. [ABSTRACT FROM AUTHOR]

In this work, we assess the effect of predictive maintenance on the reliability of repairable systems. In reparable systems, the reliability assessment considering corrective and preventive maintenance is well defined, but not considering predictive one. The effect of predictive maintenance is taken into account through a finite probability that the system is found in degraded condition. If the predictive maintenance evidences the degradation, then the repair is carried out. All the repairs are considered perfects, which means that the system is restored to an as-good-as-new condition each time the repair is carried out. The measure of system reliability under predictive maintenance is found to be between that of a maintenance-free strategy and preventive maintenance. The effect on the system reliability depends on the value of the probability that the system is found in degraded condition. We propose an expression for the reliability that is convenient when values of such probability are less than 15%. By using the proposed expression, one can estimate how much improvement in reliability any system gains by the predictive maintenance.

Functionally graded materials are a class of multifunctional materials that exhibit spatial variation in composition and microstructure. This deliberate variation is designed to effectively manage and manipulate changes in thermal, structural, or functional qualities within the material. The current study explores the system reliability of functionally graded porous beams (FGPB) subjected to buckling, focusing on the interplay of gradient index (0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10), aspect ratio (5, 10, 15, 20, 25, 30 and 35), porosity index (0, 0.1 and 0.2) and boundary conditions (simply supported, clamped–clamped and clamped-free), with a particular emphasis on the k-out-of-n system reliability assessment, employing three distinct methods such as Evolutionary, GRG Non-linear, and Lagrangian. The results show the system's sensitivity to changes in distribution parameters, with the Lagrangian method being the most robust and stable. The 10-out-of-12 system reliability analysis demonstrates that aspect ratio and even porosity distribution are reliable, while uneven porosity is not. This study enhances structural reliability analysis in FGPBs, enhancing system reliability considerations and paving the way for further advancements in structural reliability analysis.