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Wireless sensor networks have become pervasive in various applications, including environmental monitoring, smart cities, precision agriculture, and healthcare. In particular, linear wireless sensor networks that span considerable distances are increasingly deployed for applications such as pipeline monitoring and transportation systems. However, ensuring the reliability of long wireless sensor networks poses significant challenges due to the unique characteristics of the network topology and the constraints imposed by the resource-constrained sensor nodes. There is a notable lack of methods for the analysis and optimization of the reliability of large-scale sensor networks, and in this paper, we partially fill this gap. We propose a method for assessing the tradeoff between redundancy of forward error correction and reliable packet delivery. We also consider approaches to improve the reliability of linear sensor networks based on increasing topology connectivity and provide a corresponding method for the exact calculation of the all-terminal reliability. Numerical experiments demonstrate the relevance and effectiveness of our results. This research contributes to a deeper understanding of the underlying principles governing the reliability of long wireless sensor networks and provides valuable insights for the design, optimization, and management of such networks in real-world applications.

The last few decades have witnessed a dramatic evolution of Artificial Intelligence (AI) algorithms, represented by Deep Neural Networks (DNNs), resulting in AI-enabled systems being significantly dominant in various fields, including robotics, healthcare, and mobility. AI-enabled systems are currently used even for safety-critical applications, including automated driving, where they encounter reliability challenges from both hardware (HW) and software (SW) perspectives. However, there is no effective technique available that can diagnose HW and SW of AI-enabled systems in real-time during operation. Therefore, this paper proposes an intelligent real-time diagnostic technique for detecting HW and SW anomalies of AI-enabled systems and continuously improving the SW quality during operation. The proposed technique can detect HW anomalies to avoid unexpected changes in AI parameters and subsequent AI performance degradation using single context data with a detection accuracy of more than 92%. The proposed technique can also detect SW anomalies and identify edge cases in real-time, which could result in performance degradation by more than 50% compared to normal conditions. The identified edge cases can be used to continuously enhance the SW quality. Experimental results show the effectiveness of the technique for practical applications and thus can contribute to realize reliable and improved AI-enabled systems.

Considering the zoned shock effeteness, a multi-degradation system model subject to dependent competing failure processes is developed in this paper. The multi-degradation is constructed by the degradations in both a soft failure model and a hard failure model. Reliability assessment of the soft failure model has been widely studied, but degradation in a hard failure process with an extreme shock model has been rarely studied. The existing methods adopted approximate approaches to evaluate extreme shock model reliability with threshold degradation, which could not satisfy the accuracy requirement of system reliability calculation. To improve the calculation accuracy, a multi-degradation model subject to stochastic-dependent competing failure processes is investigated in this paper. A distribution-based algorithm is developed to calculate the extreme shock model reliability with threshold degradation. Furthermore, according to the effect of the shock process in the hard failure process, threshold degradation can be divided into degradation caused by both the zoned shock process and general degradation. Consequently, the closed-form formulas for system reliability with the multi-degradation model are derived. A case study is employed to illustrate the developed model, and the effectiveness of the algorithm is validated through Monte Carlo simulations.

Empirical life tests are used for reliability demonstration and determination of the actual reliability of the product. Therefore, engineers are faced with the challenge of selecting the most suitable test strategy out of the possible many and also the optimal parameter setting, e.g. sample size, in order to realize reliability demonstration with limited costs, time and with their available testing resources. It becomes even more challenging due to the stochastic nature of failure times and necessary cost and time being dependent on those. The considerations and guidelines in this paper are intended to simplify this process. Even simple products can fail due to several causes and mechanisms and usually have several components and subsystems. Therefore, this paper provides test planning options for single critical failure mechanisms as well as for systems with multiple failure mechanisms. For this purpose, the Probability of Test Success (Statistical Power of a life test) is used as a central, objective assessment metric. It is capable of indicating the probability of a successful reliability demonstration of a test and thus allows, for example, to answer the question of the required sample size for failure-based tests. The main planning resource is prior knowledge, which is mandatory due to the stochastic lifetime, in order to provide estimates for the Probability of Test Success at all. Therefore, it is also shown how to deal with uncertain prior knowledge and how the underlying information can additionally be used to increase the Probability of Test Success using Bayes’ theorem. The guidelines show how the most efficient test can be identified in the individual case and for individual boundary conditions.

In the dynamic landscape of electronic commerce, the robustness of platforms is a critical determinant of operational continuity and trustworthiness, necessitating innovative approaches to fault tolerance. This study pioneers an advanced strategy for enhancing fault tolerance in e-commerce systems, utilizing non-positional numbering systems (NPNS) inspired by the mathematical robustness of the Chinese Remainder Theorem (CRT). Traditional systems rely heavily on positional numbering, which, despite its ubiquity, harbors limitations in flexibility and resilience against computational errors and system faults. In contrast, NPNS, characterized by their independence, equitability, and residue independence, introduce a transformative potential for system architecture, significantly increasing resistance to disruptions and computational inaccuracies. Our discourse extends beyond theoretical implications, delving into practical applications within contemporary e-commerce platforms. We introduce and elaborate on new terminologies, concepts, and a sophisticated classification system for fault-tolerance mechanisms within the framework of NPNS. This nuanced approach not only consolidates understanding but also identifies underexplored pathways for resilience in digital commerce infrastructure. Furthermore, this research highlights the empirical significance of adopting NPNS, offering a methodologically sound and innovative avenue to safeguard against system vulnerabilities. By integrating NPNS, platforms can achieve enhanced levels of redundancy and fault tolerance, essential for maintaining operational integrity in the face of unforeseen system failures. This integration signals a paradigm shift, emphasizing proactive fault mitigation strategies over reactive measures. Conclusively, this study serves as a seminal reference point for subsequent scholarly endeavors, advocating for a shift towards NPNS in e-commerce platforms. The practical adaptations suggested herein are poised to redefine stakeholders’ approach to system reliability, instigating a new era of confidence in e-commerce engagements.

Scenario: System reliability monitoring focuses on determining the level at which the system works as expected (under certain conditions and over time) based on requirements. The edge computing environment is heterogeneous and distributed. It may lack central control due to the scope, number, and volume of stakeholders. Objective: To identify and characterize the Real-time System Reliability Monitoring strategies that have considered Artificial Intelligence models for supporting decision-making processes. Methodology: An analysis based on the Systematic Mapping Study was performed on December 14, 2022. The IEEE and Scopus databases were considered in the exploration. Results: 50 articles addressing the subject between 2013 and 2022 with growing interest. The core use of this technology is related to networking and health areas, articulating Body sensor networks or data policies management (collecting, routing, transmission, and workload management) with edge computing. Conclusions: Real-time Reliability Monitoring in edge computing is ongoing and still nascent. It lacks standards but has taken importance and interest in the last two years. Most articles focused on Push-based data collection methods for supporting centralized decision-making strategies. Additionally, to networking and health, it concentrated and deployed on industrial and environmental monitoring. However, there are multiple opportunities and paths to walk to improve it. E.g., data interoperability, federated and collaborative decision-making models, formalization of the experimental design for measurement process, data sovereignty, organizational memory to capitalize previous knowledge (and experiences), calibration and recalibration strategies for data sources.

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.

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.

System reliability is one of the performance criteria commonly used in the literature to design and analyze complex engineering systems such as communication networks or server infrastructures and electronic devices. Some of the infrastructure designs that affect industrial processes or even social life are formulated as optimization problems with reliability criteria among the objectives. Such design problems have attracted the attention of many researchers in different disciplines. One commonly studied problem of this group in the literature is the Redundancy Allocation Problem (RAP), which can be defined as the design of a system targeting a high reliability using redundant components in a series-parallel arrangement. The system reliability is obtained with an appropriate method depending on the system characteristics. In this study, a method is proposed for system reliability optimization with a Genetic Algorithm (GA) that uses a Discrete Event Simulation (DES) model to estimate system reliability considering increasing failure and repair rates, reflecting realistic scenarios. The validity of the DES model has been demonstrated on the test problems that are commonly used in the RAP literature. Results show that system designs with higher reliability values at lower costs, where failure and repair scenarios are considered, can be obtained with this realistic approach. [ABSTRACT FROM AUTHOR]

The complexity of large-scale systems has greatly increased and the tools for monitoring complex systems and ensuring their reliability have also been vastly studied. This study presents a tool to improve the understanding of the state-of-health of complex systems with several subsystems and components and partial observability. Many real systems can have issues with monitoring due to the cost of monitoring or physical limitations for constant monitoring. The proposed method in this study tackles this issue by utilizing all available, limited data to better predict system performance. A Multi-scale Multi-Fidelity Bayesian Learning framework is proposed to provide an online tool for component failure prediction as well as system prognostics. A Dynamic Bayesian Network is used to model the dependencies between components in a system and their impact on system failure. Multi-Fidelity Networks is then used to continuously update the parameters in the network to leverage online data to improve the predictive results. A case study of the leakage failure mode of an offshore production well is conducted to highlight the applicability as well as the benefit of the proposed approach. The results show the advantage of accounting for high-fidelity data to inform low-fidelity data for complex systems. • A new Bayesian learning method is developed for reliability analysis. • A dynamic Bayesian network is employed for system modeling. • Multi-fidelity network is integrated with a dynamic Bayesian network for multiscale system failure analysis. • Multi-fidelity data can be used for conditional probability of failure analysis. • A case study is carried out for production well reliability analysis. [ABSTRACT FROM AUTHOR]

Increasing demand for power generation, slow lessening of fossil fuel resources, and carbon footprint enhancement because of their continuous maltreatment have increased the demand for different energy sources for energy generation. Wind energy systems generate power with a reduction in environmental pollution as well as it improves the reliability and stability of the power system. For long-term planning and future capacity expansion and to ensure that the installed generation capacity is sufficient to meet the load demand, the evaluation of generation system reliability indices is a key factor. Hence, this paper proposed the innovative four-parameter-based progressive alpha model method (FPBPAMM) based on generalized functions analysis for improving the power system reliability with the insertion of wind energy generation. Therefore, the effect of the penetration of wind energy generation on the reliability of the power system is examined. Moreover, power system reliability enhancement using the wind energy system is calculated. Full risk analysis and case study have been performed to explain the effectiveness of the proposed technique and to provide a good understanding of the power system reliability impact consequential for the wind power system merging into the main grid. [ABSTRACT FROM AUTHOR]

The reliability of folding wing deployment performance greatly impacts flight vehicle reliability. Based on the dynamic analysis theory, the deployment dynamic model of folding wing mechanism with joint clearances is established and solved. Considering the failure correlation, the system reliability models are developed for both cases, without considering synchronization and considering synchronization. For the former, a solution method combining saddle point approximation and numerical integration is proposed. For the latter, an estimation method based on a combination of the fourth order moment Pearson distribution family and the numerical integration is proposed. The efficiency and accuracy of the proposed methods are verified through examples. In addition, the trend of the system reliability change when the distribution parameters of random variables are different is also analyzed. From the perspective of improving reliability, the above study can provide theoretical guidance and data support for the design, manufacturing and service process of the folding wing mechanism system. [ABSTRACT FROM AUTHOR]

The reliability of the systems as well as its optimization is the first concern of the designers. The elements of a given system can be either in series, parallel, parallel-series, or in a complex configuration. This paper addresses the reliability optimization of a natural gas processing facility. The reliability of this system is calculated and two redundancies strategies, active and standby, are optimized under the resource limits to improve reliability. Two bio-inspired optimization algorithms, namely the particle swarm optimization (PSO) and the differential evolution (DE), are implemented with penalty functions to find the optimal redundancy. The results obtained are compared. [ABSTRACT FROM AUTHOR]

Many systems connected in series are subject to failure earlier in their design life. The failure rate of such systems has been described by a two-sided power distribution, which enables modeling the entirety of the bathtub curve failure modes with a single distribution. We propose a time-dependent reliability optimization model for improving a system's reliability through the optimal allocation of redundant components whose failure distribution follows a two-sided power distribution. To solve the multi-criteria optimization model, we propose an algorithm that (i) identifies the scale tolerance at which a selected component will have a higher mean residual life, (ii) identifies the level of redundancy for enhancing system reliability, and (iii) identifies other optimal system characteristics, including minimized cost and minimized volume. Two sided power (TSP) distribution with four parameters (a, b, c, η) models time-to-failure. We consider an n-stage series system and assign redundancy within the stages. We propose a multi-objective optimization problem to assign redundancy, accounting for reliability, cost, and system volume. An illustrative example is solved using the NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

In the whole structure connected by bolt group， the reliability of the bolt group directly affects the reliability of the whole structure. The traditional reliability analysis method of the bolt group considers that the bolt parameters are determined， but in actual working conditions， the bolt parameters are uncertain， resulting in a large error. In order to accurately evaluate the reliability of the bolts group， taking a grinding robot grinding head bolt group as an example， a finite element analysis model of the grinding head is establisted. This model is used for static analysis. The accuracy of the bolt connection finite element mesh model is verified by the bolt connection experiment. The bolt group is regarded as a system， and the influence of the bolt parameter randomness is considered. The limit state function is established based on whether the maximum stress value of the bolt exceeded the yield limit. The reliability analysis is carried out by combining the Kriging model fitting method， and then the variation rule of the system reliability with the bolt preload is explored. The results show that the reliability of the bolt system is affected by random factors and determined by multiple bolts， and decreases with the increase of the bolt preload. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of electronic commerce, the robustness of platforms is a critical determinant of operational continuity and trustworthiness, necessitating innovative approaches to fault tolerance. This study pioneers an advanced strategy for enhancing fault tolerance in e-commerce systems, utilizing non-positional numbering systems (NPNS) inspired by the mathematical robustness of the Chinese Remainder Theorem (CRT). Traditional systems rely heavily on positional numbering, which, despite its ubiquity, harbors limitations in flexibility and resilience against computational errors and system faults. In contrast, NPNS, characterized by their independence, equitability, and residue independence, introduce a transformative potential for system architecture, significantly increasing resistance to disruptions and computational inaccuracies. Our discourse extends beyond theoretical implications, delving into practical applications within contemporary e-commerce platforms. We introduce and elaborate on new terminologies, concepts, and a sophisticated classification system for fault-tolerance mechanisms within the framework of NPNS. This nuanced approach not only consolidates understanding but also identifies underexplored pathways for resilience in digital commerce infrastructure. Furthermore, this research highlights the empirical significance of adopting NPNS, offering a methodologically sound and innovative avenue to safeguard against system vulnerabilities. By integrating NPNS, platforms can achieve enhanced levels of redundancy and fault tolerance, essential for maintaining operational integrity in the face of unforeseen system failures. This integration signals a paradigm shift, emphasizing proactive fault mitigation strategies over reactive measures. Conclusively, this study serves as a seminal reference point for subsequent scholarly endeavors, advocating for a shift towards NPNS in e-commerce platforms. The practical adaptations suggested herein are poised to redefine stakeholders' approach to system reliability, instigating a new era of confidence in e-commerce engagements. [ABSTRACT FROM AUTHOR]

Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength. [ABSTRACT FROM AUTHOR]

Hybrid free‐space optical and radio frequency (FSO/RF) systems have attracted much attention for more than a decade because of their enhanced reliability and capacity as comparison to single FSO or RF systems. Existing schemes to construct such a hybrid system are usually to use either switching or encoding/decoding strategies for combination of channels. The proposed scheme, based on packet sharing strategy of Ethernet link aggregation, provides a different way to combine FSO and RF channels. Theoretical analysis and Experimental results show that the proposed scheme does enhance the reliability and utilize both channel capacities for a multi‐channel system. Moreover, this scheme is simple and scalable because more channels can be combined based on such a packet sharing strategy without modifying the physical channel structures, therefore, this scheme may provide a good solution for constructing an efficient and cost‐effective hybrid FSO/RF system. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of achieving a balance between reliability and cost in system design, considering the uncertainties often present in real-world situations. It introduces a methodology that utilizes the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to coordinate reliability and cost objectives. Unlike traditional approaches that treat the problem as a single objective and disregard conflicting objectives, the proposed methodology leverages fuzzy set theory to handle uncertainties effectively. The step-by-step methodology is supported by an illustrative example and is compared to NSGA-II with non-coordination and the weighted sum approach. By using NSGA-II, the methodology generates a set of Pareto-optimal solutions, considering the conflicting nature of reliability and cost. This research contributes to decision-making techniques in system design by providing an efficient approach for handling uncertainties and optimizing trade-offs between reliability and cost. [ABSTRACT FROM AUTHOR]

Over the last four decades, researchers have recognized that traditional automation has many negative consequences stemming from human out‐of‐the‐loop problems. However, there is a paucity of research to optimize function allocation problems under uncertainty by considering both human and machine influential factors. This work attempts to address this standing paucity of research by designing an optimization model with the objective function of maximizing system reliability besides balancing human workload over a mission time and keeping him/her in the control loop. To maximize system reliability, the prediction of human and machine fault rates is inspired by the human‐in‐the‐loop fault tree analysis (FTA). Furthermore, the failure probabilities of human‐related events in the proposed fault tree are estimated via fuzzy logic inference systems. A typical supervisory control task is selected to demonstrate the application and feasibility of the proposed method. In conclusion, this mathematical model as a decision support method provides guidance for automation designers to improve an automated system via systematic prediction of failures in the early stage of the design phase. [ABSTRACT FROM AUTHOR]

Selective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied. [ABSTRACT FROM AUTHOR]

Contact wires are critical components of railway overhead infrastructure which provide power for trains. This paper proposes an innovative method to determine the service life of worn contact wires under three failure modes: tension, fatigue, and attrition. The tensile stress, fatigue damage, and cross-section loss of contact wire have been modelled as stochastic processes following lognormal distribution. The first-passage probability method is then used to determine the time-dependent failure probability for contact wires under each failure mode. The service life of the contact wire is determined through system reliability analysis. This method has then been applied to a case study to provide information on behaviour evaluation of contact wire. It has been found that the service life of contact wire can be underestimated without considering the wear effect on fatigue failure mode. Also, through sensitive analysis, it has been determined that the wear rate of contact wire can affect the probability of fatigue failure considerably in the longer term. The methodology proposed in this paper can help engineers and asset managers in making optimum decisions regarding the time of repair and/or maintenance of contact wire exposed to wear. [ABSTRACT FROM AUTHOR]

In order to improve on system reliability, utilities conduct contingency scenarios to determine the impact of component outages to the network. It is usually challenging to conduct this analysis due to the complex grid structure. However, the huge consequences of an equipment outage on the grid and the connected loads are compelling enough for researchers to find ways to mitigate these events. Several blackouts have occurred in various parts of the Southern Interconnected Grid (SIG) of Cameroon due to the transient state of the power system. The most recent was the blackout that kept a greater part of the country's political capital, Yaounde, for several days with no electricity. The paper analyses 44 single line (N-1) contingency scenarios in the SIG using ETAP in order to evaluate the impact of line outage events on grid stability. A combined performance index was calculated from the bus voltage security index (PIV/Vsp), real power flow index (PIΔP), reactive power flow index (PIΔQ) and the branch overloading security index (PIS/Ssp). The results showed that the Mangombe - Oyomabang line outage (scenario 22) was the most severe with the load flow failing to converge, followed by the Oyomabang - Ngousso line 1 (scenario 36) with combined performance indices of 992.6191 and 79.74415 respectively. Other scenarios showed overloaded lines, overloaded transformers, high power losses and under/over bus voltages. This study has identified lines with high vulnerability and should be used by utility operators to enhance the reliability of the transmission network and minimize its susceptibility to cascaded power outages.

Recently, reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering. However, for deep foundation pit, evaluating the system safety of retaining structures and finding cost-effective design points are main challenges. To address this, this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou, China. The proposed method included two parts: system reliability model and robust design method. Back Propagation Neural Network (BPNN) is used to fit limit state functions and conduct efficient reliability analysis. The common source random variable (CSRV) model are used to evaluate correlation between failure modes and determine the system reliability. Furthermore, based on the system reliability model, a robust design method is developed. This method aims to find cost-effective design points. To solve this problem, the third generation non-dominated genetic algorithm (NSGA-III) is adopted. The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm. The proposed method has a good performance in locating the balanced design point between safety and construction cost. Moreover, the proposed method can provide design points with reasonable stiffness distribution.

Multi-objective design optimization problems offer a set of solution alternatives within a Pareto-front. In structural design, the design variables are typically the section properties. The outcomes of these design variables are usually used in selecting standard sections. However, the properties of the selected standard sections normally have different values from the determined design variables. Accordingly, the values of the objective functions for these solutions will change after selecting standard sections. This change may be different among these solutions. Thus far, only Pareto-optimal solutions have been standardized in the literature. The effects of the inclusion of non-Pareto solutions in the standardization process have never been examined. In this paper, the differences between including the near-optimal solutions and not including them in the final structural design optimization set are explored. The paper investigates the impact of selecting standard sections on both Pareto-front solutions and near-optimal solutions, and it studies the importance of keeping track of the near-optimal solutions. A modified version of a multiple-objective particle swarm optimization method, enhanced with a proposed computationally efficient section standardization algorithm, is used to solve multi-objective system-reliability design optimization problems. The concepts of the paper are applied to examples of bridge structures. The results demonstrate the efficiency of the proposed technique in capturing final design solutions that would have been otherwise missed due to standardization.

For the overall safety evaluation of the tension leg platform (TLP) system, the structural strength reliability analysis is essential. The strength reliability of a typical TLP exposed to combined static and extreme wave loads is the subject of this study. A three-dimensional finite element shell model made up of a pontoon, column, and deck that is assumed to be accurate and efficient with tendon system as the boundary conditions was built using a concrete TLP. By employing the proportional loading method based on the design wave method, failure mechanisms for the platform and were determined. The failure mechanisms were then used to derive limit state expressions. The system reliability indexes of the object platform were calculated using the system reliability assessment framework created by the system reliability model suggested in this paper. This work seeks to advance the application of reliability theory in offshore engineering.

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.

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.

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.

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 Hybrid free‐space optical and radio frequency (FSO/RF) systems have attracted much attention for more than a decade because of their enhanced reliability and capacity as comparison to single FSO or RF systems. Existing schemes to construct such a hybrid system are usually to use either switching or encoding/decoding strategies for combination of channels. The proposed scheme, based on packet sharing strategy of Ethernet link aggregation, provides a different way to combine FSO and RF channels. Theoretical analysis and Experimental results show that the proposed scheme does enhance the reliability and utilize both channel capacities for a multi‐channel system. Moreover, this scheme is simple and scalable because more channels can be combined based on such a packet sharing strategy without modifying the physical channel structures, therefore, this scheme may provide a good solution for constructing an efficient and cost‐effective hybrid FSO/RF system.

The paper presents an iterative approach based on stochastic simulations and adaptive Kriging metamodels to perform reliability and safety assessments of soil slopes. Two new rules for adaptively selecting support points are proposed, considering an entropy learning function and the closeness to the failure domain defined by a limit state function. In addition, a stopping criterion is proposed based on root-mean-square and mean absolute percentage errors computed with cross-validation at the local level, focusing on regions where the uncertainties are relevant. Finally, the selection rules for support points and the error metrics are implemented in two benchmark problems with a low, moderate, and high probability of failure. Ultimately, the work leads to an adaptive Kriging strategy for slope stability assessment, offering: (1) a fair comparison with other strategies based on a significant number of realizations, (2) a stopping criteria based on a new local error metric, (3) an insight of the behavior across different magnitudes of the probability of failure, and (4) a new selection rule that reduces the total number of support points significantly. The proposed scheme is easily paired with commercial software to compute support points, resulting in an attractive tool for practitioners.

The state of the Ghana Power System reflects a story of progress, challenges, and future potential. Ghana has experienced significant milestones and achievements in its power system, including the development of major infrastructure projects such as the Akosombo Dam and initiatives to expand access to electricity. The country has also made strides in diversifying its energy mix by embracing renewable energy sources. However, challenges exist, including the need to address system reliability, aging infrastructure, financial viability, and rural electrification. The integration of emerging technologies, such as smart grid solutions, energy storage systems, and regional power interconnections, offers opportunities for a sustainable and reliable power system. Recommendations for Ghana's power sector focus on diversification, grid flexibility, infrastructure upgrades, energy efficiency, institutional strengthening, and regional cooperation. Implementing these recommendations holds the promise of building a resilient, affordable, and environmentally sustainable power system for Ghana's future.

The paper explores the probabilistic interpretations of algebraic inequalities and presents several findings. First, the inequality of the additive ratios can be used to increase the probability of an event occurring within a set of mutually exclusive and exhaustive events. The interpretation of this inequality produced a counter‐intuitive result, that for suppliers delivering the same quantity of reliable products alongside unknown numbers of unreliable products, the probability of purchasing a reliable product from a randomly selected supplier is higher than the probability of purchasing a reliable product from the market formed by all suppliers. Next, the paper discusses how averaging the reliabilities of components from different varieties can lead to a significant overestimation of the calculated system reliability, as demonstrated by another algebraic inequality interpretation. Finally, the paper derives tight bounds for the reliability of demand in a load‐strength interference model by interpreting the Hayashi's inequality. Notably, these bounds do not depend on the shape of the load distribution and only require the strength distribution to be known in relatively small vicinities of the lower and upper bound of the load. [ABSTRACT FROM AUTHOR]

Cascading failure is a phenomenon that minor disturbances trigger a chain reaction failure of the system, causing the disaster to quickly spread in the system and causing part or all of the system to collapse. Traditional cascading models that based on the assumption that the propagation time of cascading failures can be ignored are inadequate for modeling and analyzing all the practical cascading failure systems. It is necessary to develop a general methodology for the cascading failure systems with cascading failure propagation speed and different load effects. This paper derives new models which extend the load effect mode and consider the propagation speed of cascading failure. In the models, the time of the cascading failure propagation is considered. Three load effect modes are proposed that consider the number of remaining working components, the influence range of the failed parts, and the distance between the failed parts and the unfailed components. Additionally, the reliability of the systems with different types of cascading failures is analyzed, and formulas computing reliability indexes are derived. Numerical examples are provided to demonstrate the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Specifically, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the first one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes. [ABSTRACT FROM AUTHOR]