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Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method: We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model’s predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions: The comparison of the empirical data to the reliability model’s predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

PurposeMaintaining a safe and available supply of blood requires a mindfully coordinated supply chain (SC) and is fundamental to the effective operation of health systems across the world. This study investigates how blood supply chain (BSC) actors demonstrate collective mindfulness (CM) principles in their operations and how these demonstrations lead to improvements in blood safety and availability (BSA) in different operational contexts.Design/methodology/approachSix case studies drawn from two contrasting BSCs, the UK and Indonesia, which differ in structure and regulation are investigated in this research. Qualitative data are collected and analysed using template analysis.FindingsThe cases reveal how the CM principles are demonstrated in the supply chain context in a range of operational conditions and their impact on BSA. The BSC actors in the more centralised and tightly regulated cases display more behaviours consistent with more of the CM principles over a greater range of operational conditions compared to those in the more decentralised and loosely regulated cases. As such, more improvements in BSA are found in the former compared to the latter cases.Originality/valueThis paper is considered the first to investigate the demonstration of CM principles at the SC as opposed to the single organisational level. It proposes an alternative approach to understanding and evaluating reliability performance using behavioural rather than statistical principles.

Fatigue is among the most critical forms of deterioration damage that occurs to steel bridges. The performance of steel bridges, which may be seriously affected due to fatigue, should be assessed and predicted. However, the code-based reliability assessment only provides a clear criterion for single details. The whole bridge or a splitting system from the bridge may contain many details which brings system impact on the fatigue assessment. In this paper the system reliability of steel bridges is addressed with focus on the influence of spatial correlations between similar fatigue sensitive details. The study is conducted on a typical riveted railway bridge, from which a subsystem is selected and modelled as a weakest link system. It is assumed that all details in the system are equicorrelated. An efficient modeling approach has been developed to quantify the correlation and help estimate the system reliability analytically. A parametric study was performed to determine the system effects on the fatigue reliability assessment and identify the decisive variables. QC 20210810

The Internet of Things (IoT) is a network topology structure based on the interconnection of many nodes. It realizes the basic functions of IoT through the transmission of information, data, and energy between the nodes. To study the reliability of Internet of Things Network Topology (IoTNT) structure, we must abstract IoT as network topology and study the reliability of the network itself from the topology structure. This paper attempts to apply the Space Fault Network (SFN) to the study the reliability of IoTNT. To achieve this goal, the nodes and edges of IoTNT are equivalent to events and connections of SFN respectively. A structure analysis method based on SFN is proposed and used to study the reliability of IoTNT. At the same time, the influence of possible logical relationship between nodes on the reliability of IoTNT is studied. According to the SFN structure representation methods (SFNSRMs), considering different network structures and induced modes, the analysis methods and calculation methods of the evolution process of target event are given. An example is given to illustrate the analysis and calculation process. The research provides the new methods for the reliability study of IoT and the development of SFN.

This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independent control protocols, namely head harvesting energy of each timeslot (HHT) and head harvesting energy of dedicated timeslot (HDT). Additionally, for complex channel states, we derive the cumulative distribution function (CDF) of packet transmission time using selective combining (SC) and maximum ratio combining (MRC) techniques. Analytical expressions for system reliability and packet timeout probability are obtained. At the same time, we also utilize the Monte Carlo simulation method to simulate our system and have analyzed both the numerical and simulation solutions. Results show that the performance of the HHT protocol is better than that of the HDT protocol, and the MRC technology outperforms the SC technology for the HHT protocol in terms of the energy-harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio (SNR), and length of energy harvesting time.

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

Reliability and sensitivity issues are very close and important problems in any technical system. The system’s sensitivity is understood as the dependence of its behavior on changes in some internal parameters. To perform sensitivity analysis, a general procedure based on a theoretical and numerical study is proposed and applied to a repairable k-out-of-n model. The results show the asymptotic insensitivity of the non-stationary and stationary characteristics of the system reliability to the shape of the repair-time distribution, as well as to the value of its coefficient of variation at a fixed mean. The proposed methodology can be useful to researchers and engineers at the designing stage of real systems, as well as applied to other stochastic reliability models.

Maintaining the supply chain in every industry is an important concern for the operators. The negative impacts of inappropriate maintenance could be discussed from different perspectives as well as capital loss, reputation loss, hazard and risk for lives, etc. In recent years, District heating (DH) in the countries that employing this technology broadly, turned to a vital energy infrastructure for delivering heat from suppliers to the consumers. Therefore, the reliability of the system is of high importance for the public interest. The transition from reactive maintenance to proactive maintenance have improved a lot the reliability to the system. Currently, many industries are exploiting different forms of artificial intelligence (AI) to predict the failures and plan for interventions to increase the system efficiency. In this paper the different methods of predictive maintenance have been reviewed and the compatibility to apply on a DH network has been discussed.

The modern evolution of technological systems from Hierarchical branching structures purposed to centralized transfer and distribution of limited resources to multi-agent interconnected self-organized networks aimed to produce, transport and consumption of resources are considered. The model of multi-agent interconnected self-organized adaptive networking systems is proposed, the network topology is considered, a system functioning model including transient processes is analyzed. A substantial limitation of the traditional reliability paradigm for a novel type of systems is demonstrated. It was assumed, that optimization approaches in the context of “big data” utilization lead to create a quasi-infinite space of non-structured decisions, which can be characterized as “big decisions”. The modified approach based on the “equally defended networked system” paradigm and the corresponding quantitative risk measure is proposed.

A notable reliability model is the binary threshold system (also called the weighted-k-out-of-n system), which is a dichotomous system that is successful if and only if the weighted sum of its component successes exceeds or equals a particular threshold. The aim of this paper is to extend the utility of this model to the reliability analysis of a homogeneous binary-imaged multi-state coherent threshold system of (m+1) states, which is a non-repairable system with independent non-identical components. The paper characterizes such a system via switching-algebraic expressions of either system success or system failure at each non-zero level. These expressions are given either (a) as minimal sum-of-products formulas, or (b) as probability–ready expressions, which can be immediately converted, on a one-to-one basis, into probabilities or expected values. The various algebraic characterizations can be supplemented by a multitude of map representations, including a single multi-value Karnaugh map (MVKM) (giving a superfluous representation of the system structure function S), (m+1) maps of binary entries and multi-valued inputs representing the binary instances of S, or m maps, again of binary entries and multi-valued inputs, but now representing the success/failure at every non-zero level of the system. We demonstrate how to reduce these latter maps to conventional Karnaugh maps (CKMs) of much smaller sizes. Various characterizations are inter-related, and also related to pertinent concepts such as shellability of threshold systems, and also to characterizations via minimal upper vectors or via maximal lower vectors.

Soft open points (SOPs) have the ability to regulate the power flow among their terminals in a continuous manner, which can solve the problems brought by the synergy of distribution networks and distributed generation, and improve the system reliability and power supply capability. To address the current lack of quantitative measurement on the effects of SOPs, the power supply capability evaluation method for active distribution networks (ADNs) with four-terminal SOPs considering reliability is proposed. Firstly, the topology and configuration modes of four-terminal SOPs are studied, and their control modes are investigated in normal operation and supply restoration conditions. Then, using the feeder partition method, the effects of four-terminal SOPs on the power outage duration of different load areas after a fault are studied, and the reliability evaluation process for ADNs with four-terminal SOPs is developed based on the quasi-Monte Carlo method. Later, with reliability as the main constraint, the power supply capability evaluation model for ADNs with four-terminal SOPs is established, and the solution algorithm is proposed. Finally, the effectiveness and applicability of the method proposed are verified through a case study.

Purpose. Development of a model of perspective management of production systems of the construction industry on the basis of general laws and principles of systemology on the way to achieving the system of the final definite result – organizational and technological reliability of construction. Methodology. Use of methods of system analysis and systematic justification, use of methods of systems theory, use of methods of modeling theory with the purpose of perspective management of production systems of the construction industry on the basis of general laws and principles of systemology on the way to achieving the system of final definite result – organizational and technological reliability of construction. Findings. The influence of the calendar schedule parameters on the reliability of construction of residential buildings in relation to the management and implementation of the project is substantiated; performed statistical tests allow to take into account the probabilistic nature of construction and the influence of random factors on the process of building houses; factors of low level of organizational and technological reliability of designing and probability of reduction of planned profit, which are hidden in financial, time, space and organizational losses, are revealed and substantiated. Originality. The optimization model of improvement of the general methodology for the effective realization of organizational and technological capabilities of production systems to ensure organizational and technological reliability of construction is proposed. Practical value. The use of optimization measures to improve organizational and technological reliability is proposed to be implemented in accordance with the developed structural and logical scheme, which determines the priority directions of the prospective strategy of management activities in the context of systematic understanding., Цель. Разработка модели перспективного управления производственными системами строительной отрасли на основе общих законов и принципов системологии на пути достижения системой конечного определенного результата – организационно-технологической надежности строительства. Методика. Использование методов системного анализа и системотехнического обоснования, использование методов теории систем, использования методов теории моделирования с целью перспективного управления производственными системами строительной отрасли на основе общих законов и принципов системологии на пути достижения системой конечного определенного результата – организационно-технологической надежности строительства. Результаты. Обосновано влияние параметров календарного графика на надежность возведения жилых зданий, во взаимосвязи с управлением и реализацией проекта; выполнены статистические испытания позволяют учесть вероятностный характер строительства и влияние случайных факторов на процесс возведения домов; выявлены и обоснованы факторы низкого уровня организационно-технологической надежности проектирования и достоверности снижения плановой прибыли, которые содержаться в финансовых, временных, пространственных и организационных потерях. Научная новизна. Предложенная оптимизационная модель совершенствования общей методики для эффективной реализации организационно-технологических возможностей производственных систем по обеспечению организационно-технологической надежности строительства. Практическая значимость. Использование оптимизационных мероприятий по повышению организационно-технологической надежности предложено реализовывать по разработанной структурно-логической схеме, которая определяет приоритетные направления перспективной стратегии управленческой деятельности в контексте системологического понимания. Выводы. Выполнено системотехническое прогнозирования сроков возведения жилых объектов на основе вероятностно-статистического подхода. Перспективность управленческой стратегии, усовершенствованная аналитическим исследованием влияния напряженности работ календарного графика на надежность выполнения строительного проекта и разработанными рекомендациями по оптимизации организационно-технологического проектирования и уменьшения степени риска подрядной организации в процессе функционирования производственных систем строительства., Мета. Розробка моделі перспективного управління виробничими системами будівельної галузі на основі загальних законів і принципів системології на шляху досягнення системою кінцевого визначеного результату – організаційно-технологічної надійності будівництва. Методика. Використання методів системного аналізу та системотехнічного обґрунтування, використання методів теорії систем, використання методів теорії моделювання з метою перспективного управління виробничими системами будівельної галузі на основі загальних законів і принципів системології на шляху досягнення системою кінцевого визначеного результату – організаційно-технологічної надійності будівництва. Результати. Обґрунтовано вплив параметрів календарного графіку на надійність зведення житлових будівель, у взаємозв’язку із управлінням та реалізацією проекту; виконані статистичні випробування дозволяють врахувати імовірнісний характер будівництва і вплив випадкових факторів на процес зведення будинків; виявлені та обґрунтовані чинники низького рівня організаційно-технологічної надійності проектування і вірогідності зниження планового прибутку, які криються в фінансових, часових, просторових і організаційних втратах. Наукова новизна. Запропонована оптимізаційна модель вдосконалення загальної методики з ефективної реалізації організаційно-технологічних можливостей виробничих систем по забезпеченню організаційно-технологічної надійності будівництва. Практична значимість. Використання оптимізаційних заходів з підвищення організаційно-технологічної надійності запропоновано реалізовувати за розробленою структурно-логічною схемою, яка визначає пріоритетні напрямки перспективної стратегії управлінської діяльності у контексті системологічного розуміння. Висновки. Виконано системотехнічне прогнозування термінів зведення житлових об’єктів на основі імовірнісно-статистичного підходу. Перспективність управлінської стратегії вдосконалена аналітичним дослідженням впливу напруженості робіт календарного графіку на надійність виконання будівельного проекту та розробленими рекомендаціями з оптимізації організаційно-технологічного проектування і зменшення ступеня ризику підрядної організації у процесі функціонування виробничих систем будівництва.

The near-sea off-shore oil extraction and transportation system use heterogeneous fleets to transfer crude oil from the floating production storage and offloading to the land-based oil storage port. Based on the characteristics of this system, the short sea inventory routing problem is investigated considering the shuttle tanker fleet and inventory management. In order to minimize the total operation cost and maximize the system reliability, a semi-continuous model for the shuttle tanker scheduling problem is established. The model optimizes the tanker scheduling plan and the design of the tanker fleet. To solve the complex model, this article proposes an improved non-dominated sorting genetic algorithm with differential evolution operator to solve the optimization of the multi-objective model. This research also uses public vessel operation data to test the modeling and optimizing efficiency. The Pareto Fronts associated with the total operation cost and the system reliability from the optimization outcome is analyzed to provide scheduling priority advice. The results indicate that proposed optimization algorithms are effective, and the operation could be optimized with the proposed model and algorithm. Refereed/Peer-reviewed

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Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies.

Everyday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic assumptions, sometimes at the expense of accuracy. Simulation-based methods, on the other hand, can account for these realistic operational attributes but are computationally intensive and usually system-specific. This chapter introduces two novel simulation methods: load flow simulation and survival signature simulation which together address the limitations of the existing analytical and simulation methods for the reliability analysis of large systems.

Based on the characteristics of drone swarm such as low cost, strong integrity, and frequent information exchange, as well as the high cost of timely maintenance of traditional units. This paper proposes a swarm maintenance method based on the reliability assessment of multi-layer complex network missions, which combines the multi-layer complex network system evaluation method with the group maintenance method. On the basis of considering the problem of maintenance grouping cost, the failure mechanism of drones in different modes and the impact of drone maintenance on the system are studied. According to the failure model of single node, the complex network method is used to establish the swarm system’s topology model and evaluate the system mission reliability. The maintenance grouping strategy is optimized by using the multi-objective planning of cost and system mission reliability. Compared with the existing just in time maintenance methods, this method can greatly reduce the total maintenance cost of the swarm system maintenance under the condition of ensuring the high mission robustness of the swarm. In addition, a universal drone swarm mission scenario is used to illustrate the method, and the results verify the feasibility and effectiveness of the method.

Various random factors in the bridge construction process directly affect the safety of the bridge life cycle. The existing theories on the reliability of bridge structure mainly focus on the reliability of components and the reliability of the bridge structure system in the completion and operation stages, while the research on the reliability of the structure system in the construction stage is relatively lacking. Therefore, this paper proposed using the Copula function to calculate the reliability index of the bridge structure construction process system. The basic theory of the Copula function was introduced in detail, and the formula was improved according to the actual situation of bridge construction. Finally, the sensitivity analysis of bridge system reliability was carried out. The research results showed that the method proposed in this paper based on Copula theory to calculate the reliability index of the bridge structure construction process system has strong applicability, simple calculation, and can be used in conjunction with the “interval estimation method”, which is suitable for large and complex bridge structural engineering. At the same time, the conclusion that the influence of failure mode correlation on structural reliability should not be ignored in the actual engineering construction process is confirmed.

The results of calculations of construction brick destruction probability determination at central and off-center compressionare given; the determination of the brick structures destruction probability, caused by the exhaustion of the masonry strengthon local compression (crushing); determination of the brick structures destruction probability associated with the exhaustionof the masonry strength on the displacement (cut); determination of the brick structures destruction probability associatedwith the exhaustion of the masonry strength on the fold, the stretch; determination of the brick structures destruction probabilityon the crack opening. The value of the security characteristic for each case has been determined and the comparisonwith the normative values has been made.

The modern power system is becoming more complex and dynamic because of increasing penetration of renewable energy resources, operating closer to system capacity for economic benefits. In order to maintain the system stability, the system operator is required to initiate appropriate preventive control action under severe contingencies while satisfying the system operating constraints. Real time transient stability assessment (TSA) of power system is proposed in this paper by using wide area measurement system (WAMS) and phasor measurement units (PMUs). An architecture based on least square support vector machine (LS-SVM) has been developed to identify the system stability state in real time. Also, this paper proposes a coherency based selection method to identify the appropriate members for generation rescheduling as preventive operation in insecure operating contingency. Rotor angle trajectories based transient stability index (TSI) is employed to classify the generators as either critical or non-critical generators in a power network. Accordingly preventive control action in the form of generation rescheduling can be initiated to achieve stability. The method has been demonstrated on the IEEE 10-machines, 39-bus system. The proposed methodology is effective and capable of handling complex power system models with multiple contingencies.

Control-system robustness verification with respect to implementation aspectslacks automated verification approaches for checking stability and performanceof uncertain control systems, when considering finite word-length (FWL) effects.Here we describe and evaluate novel verification procedures for digital systemswith uncertainties, based on software model checking and satisfiability modulotheories, named as DSVerier v2:0, which is able to check robust stability ofclosed-loop control systems with respect to FWL effects. In particular, wedescribe our verification algorithms to check for limit-cycle oscillations (LCOs),output quantization error, and robust non-fragile stability on common closedloop associations of digital control systems (i.e., series and feedback). DSVerier v2:0 model checks new properties of closed-loop systems (e.g., LCO), including stability and output quantization error for uncertain plant models, and considers unknown parameters and FWL effects. Experimental results over a large set of benchmarks show that 35%, 34%, and 41% of success can be reached for stability, LCO, and output quantization error verification procedures, respectively, for a set of 396 closed-loop control system implementations and realizations.

System reliability of an offshore power plant with several gas turbine engines is analyzed in this study to understand the failure intensity of a selected gas turbine engines under varying maintenance activities. A set of event data of a selected gas turbine engine is considered to identify system failure intensity levels, where unknown maintenance actions were implemented (i.e. various repairs disturb the failure rate). A non-homogeneous Poisson process (NHPP) is used to model the age dependent failure intensity of the same gas turbine engine and the maximum likelihood estimation (MLE) approach for calculating the respective model parameters is proposed. Several failure intensity rates (i.e. varying failure trends) in these models (i.e. during the system age of the gas turbine engine) are observed. Furthermore, these varying failure trend models are evaluated with actual failure events of the same gas turbine engine by considering two goodness-of-fit tests: Cramer-von Mises and Chi-square tests. Finally, system reliability of the gas turbine engine under the failure transition, failure intensity, mission reliability and mean time between failures (MTBF) is also discussed in this study.

The system signature calculated independently from the distribution of the lifetime of the units in the analysis of technical systems enables us to have important information about the reliability of the system. System signature is a probability vector obtained from possible sequences of units according to the moments of deterioration. The reliability of the system can be easily obtained if the distribution of the lifetime of the units is known and if the distributions can be listed as open among themselves. However, when the distributions cannot be listed as open among themselves, it is a very important finding that the system reliability is calculated with the help of system signature by generating the order statistics. It is also possible to calculate the odds ratios of the units when the lifetime of the units is certain. In this study, odds ratio could be calculated by calculating the probability of fail the system of each unit by considering the possible situations of the system, as in the system signature, regardless of the distribution of lifetime of the units. In addition, the technical system is represented by a matrix by establishing a relation between the system fail probabilities of the units obtained in the study and the system signature. However, examples of some technical systems given in the study.

The geometric programming problem (GPP) is a beneficial mathematical programming problem for modeling and optimizing nonlinear optimization problems in various engineering fields. The structural configuration of the GPP is quite dynamic and flexible in modeling and fitting the reliability optimization problems efficiently. The work’s motivation is to introduce a bounded solution approach for the GPP while considering the variation among the right-hand-side parameters. The bounded solution method uses the two-level mathematical programming problems and obtains the solution of the objective function in a specified interval. The benefit of the bounded solution approach can be realized in that there is no need for sensitivity analyses of the results output. The demonstration of the proposed approach is shown by applying it to the system reliability optimization problem. The specific interval is determined for the objective values and found to be lying in the optimal range. Based on the findings, the concluding remarks are presented.

International audience; For dealing with uncertainty in Remaining Useful Life (RUL) predictions, numerous studies in literature use stochastic models to characterize the degradation process and predict the RUL distribution. However, in practice, it is difficult to derive stochastic models to capture degradation mechanisms of complex physical systems. Besides, the outstanding achievements in sensing technologies have facilitated the development of data-driven methods. Among them, deep learning methods become one of the most popular trends in recent studies; but they usually provide point predictions without quantifying the output uncertainties. In this paper, we present a new probabilistic deep leaning methodology for uncertainty quantification of multi-component systems' RUL. It is a combination of a probabilistic model and a deep recurrent neural network to predict the components' RUL distributions. Then, using the information about the system's architecture, the formulas to quantify system reliability or system-level-RUL uncertainty are derived. The performance of the proposed methodology is investigated through the benchmark data provided by NASA. The obtained results highlight the point prediction accuracy and the uncertainty management capacity of the proposed methodology. In addition, thanks to the explicit RUL distributions of components, the system reliability for different structures is obtained with high accuracy, especially for series structures.

Interconnection of DERs into the transmission lines is starting to take a substantial share of the total power capacity. Although the largest share of power generation attributes to coal and gas power plants, renewable energy is gradually increasing. However, in the past, the size of DERs was relatively smaller, and rooftop PV was the dominant renewable energy source. As a result, the studies for interconnection focused on those rooftop PVs on the distribution side. Since the scenario is slowly changing as more utilities increase the share of clean energy by building large-scale solar farms and wind farms, it is necessary to study the effect of those DERs in the transmission system. Among the various issues, this work focuses on the impact on a transmission system's voltage stability. When the voltage stability at a point in the system is compromised, it can affect the entire power system's overall security, quality, and reliability. Therefore, this work aims to assess the system's stress due to increased loading conditions and increased growth of DERs integration. A steady-state voltage stability index is used to generate a heat-map that identifies the areas where the system can go unstable in events like the loss of the renewable generation under a bus. The steady-state simulation is performed on the IEEE 14 bus system in Distributed Engineering Workstation (DEW) to find the system's weak links using the stability heat-map. DERs are added to the corresponding weak buses, and the improvement in the stability margin for various penetration levels are studied. The results obtained from the steady-state analysis are also verified using the dynamic simulation of the model using OpenModelica. Master of Science Transmission networks are going through some of the fundamental changes in how they are planned and operated as more and more renewable energy sources are connected to the grid. Unlike the traditional setup where the transmission line transfers bulk power from a large generator to the load center at a different location, the advent of renewable energy resources enables the power to be generated in distributed form. It allows electrical power to be generated closer to the demand. In the long run, the transmission system's stress reduces as a significant portion of demand is supplied locally. Thus, the distributed energy resources (DERs) in the power grid have the potential for substantial economic and environmental benefits. However, it can also bring about a range of challenges to the power system. Among the various issues, this work focuses on the effects on a transmission system's voltage stability. When the voltage stability at a point in the system is compromised, it can affect the entire power system. Therefore, this work aims to assess the stress on the system due to increased loading conditions and increased growth of DERs integration, utilizing a voltage stability index to identify the areas where the system can go unstable in events like the loss of renewable generation under a bus. The steady-state simulation is performed on the IEEE 14 bus system to find the weak links in the system where DERs can improve the system's stability. The results obtained from the steady-state analysis are verified using the dynamic simulation of the model.

In this paper, reliability of the liquid medicine manufacturing system of pharmaceutical plant named as Yaris Pharmaceuticals is enhanced on solving a redundancy allocation problem with the help of three algorithms HASL1 (Heuristic algorithm with selection factor 1), HASL2 (Heuristic Algorithm with Selection factor 2) and HASL3 (Heuristic Algorithm with Selection factor 3). It is ensured that redundancy is allocated within given cost constraints to maximize system reliability. Post allocation of redundancy the results of these algorithms are analyzed with the help of graphs, it has been found that the reliability of the system is optimized.

In this paper a new process is introduced. To some extent it has resemblance with Queueing-Inventory (Inventory with positive service time) (see Sigman and Simchi-Levy [2] and Melikov and Molchanov [1]. We consider a k - out - of - n: G system of identical components, each of which has exponentially distributed life time with parameter l, independent of the others. When the number of working components goes down to N (k ��� N ��� n) due to failures, an order for n ��� k + 1 items is placed. Replenishment time is exponentially distributed with parameter b. On replenishment, all failed components are instantaneously replaced by the new arrivals, subject to a maximum of n ��� k + 1. This process is investigated and its long run system state distribution derived explicitly. An associated optimization problem is discussed. Throughout this paper the k out of n system is assumed to be COLD.

The importance measure is a crucial method to identify and evaluate the system weak link. It is widely used in the optimization design and maintenance decision of aviation, aerospace, nuclear energy and other systems. The dissimilar redundancy actuation system (DRAS) is a key aircraft control subsystem which performs aircraft attitude and flight trajectory control. Its performance and reliability directly affect the aircraft flight quality and flight safety. This paper considers the influence of the Birnbaum importance measure (BIM) and integrated importance measure (IIM) on the reliability changes of key components in DRAS. The differences of physical fault characteristics of different components due to performance degradation and power mismatch, are first considered. The reliability of each component in the system is then estimated by assuming that the stochastic degradation process of the DRAS components follows an inverse Gaussian (IG) process. Finally, the weak links of the system are identified using BIM and IIM, so that the resources can be reasonably allocated to the weak links during the maintenance period. The proposed method can provide a technical support for personnel maintenance, in order to improve the system reliability with a minimal lifecycle cost.

Various kinds of dynamic routing architectures are used in today’s service- and cloud-based architectures, including sidecar-based routing, routing through a central entity such as an event store, or ar- chitectures with multiple dynamic routers. So far, the impact of these architectures on system reliability has not been studied. We propose an analytical model of request loss during router and service crashes, as well as an empirical validation of that model. The comparison of the empir- ical data to the predicted values by our model shows a low enough and converging error rate for using the model during system architecting. Our model predicts that, having the same crash probability, decentral- ized routing results in losing a higher number of requests in comparison to more centralized approaches. These results provide important new in- sights when making architectural decisions regarding dynamic routing. To the best of our knowledge, our study is the first to empirically study the reliability trade-off in such architectural decisions.

System reliability is defined as the probability of satisfactory performance of a system under stated conditions for a specified period of time. According to this definition, four parameters, including probability, satisfactory performance, specific conditions, and time should be exactly characterized to evaluate the system reliability accurately. However, due to the uncertainty involved in real situations, it is hardly possible to assess the aforementioned parameters precisely. In this article, two general and distinct approaches, including Zadeh's extension principle and modification of fuzzy parametric programming (FPP), are proposed to take into account such uncertainty in a famous reliability problem called the overspeed protection system. According to Zadeh's extension principle, a pair of nonlinear programming problems is formulated to compute α‐level cuts of fuzzy system reliability. The membership function of fuzzy system reliability can then be constructed analytically by numerating different values of α. This fuzzy system reliability presents flexibility for further system analysis. In the second approach, from a different point of view, a variant of FPP is improved that provides a crisp value as a system reliability measure. The rewarding point of the latter procedure is to handle the problem in a computationally easier way.

This thesis explores the periodicity of planned depot maintenance and its effects on failure rates for U.S. Coast Guard shipboard davit systems. Davits are used on Coast Guard cutters to launch small boats, which are needed to accomplish a range of missions. The operational availability of these davit systems is important in keeping Coast Guard cutters fully mission capable. This study collected davit failure and maintenance data from nearly eight years of archived records for two cutter classes, which served as a sample of Welin Lambie davit systems that are widely used in the fleet. Concepts of reliability, availability, and maintainability were applied to calculate failure rates, mean time between maintenance, and mean time between failures. From this information, statistical hypothesis testing was used to determine what factors influence the system failure rate. It also addresses whether the historical data on these systems can be used to develop experimental models and whether those models can be used to influence decisions on maintenance periodicity. Lieutenant Commander, United States Coast Guard Approved for public release. distribution is unlimited

The purpose of this paper is to assess the availability of different HVDC transmission system technologies (LCC and VSC) for different transmitted power capacities in order to compare the strengths and weaknesses of each configuration. The matrix-based system reliability (MSR) method has been used since it turns out to be one of the best methods for reliability assessment of multi-terminal HVDC (MTDC) transmission systems taking into account multiple derated states. The results of this study can be useful in the planning and designing stage of HVDC systems.

Background Scoliosis is a spine abnormal deviation, which is an idiopathic disorder among children and adolescents. As a matter of the fact, distribution of loads on the patient’s spine and load-carrying capacity of the vertebral column are both random variables. Therefore, the probabilistic approach may consider as a sophisticated method to deal with this problem. Method Reliability analysis is a probabilistic-based approach to consider the uncertainties of load and resistance of the vertebral column. The main contribution of this paper is to compare the reliability level of a normal and scoliosis spinal. To do so, the numerical analyses associated with the inherent random parameters of bones and applied load are performed. Then, the reliability indices for all vertebrae and discs are determined. Accordingly, as the main innovation of this paper, the system reliability indices of the spinal column for both normal and damaged backbone systems are represented. Results Based on the required reliability index for normal spinal curvature the target system reliability level for scoliosis disorder is proposed. Conclusion Since the proposed target reliability index is based on the strength limit state of the vertebral column, it can be considered as a reliability level for any proposed treatment approaches.

Highlights • We consider a F-policy retrial machine repair problem with working breakdowns. • One practical illustration of the online-learning cloud system is presented. • The differential equations are tabulated and solved by Laplace transform technique. • Closed-form expressions for the reliability and the MTTF are explicitly derived. • Reliability sensitivity analysis with respect to system parameters are performed. • Several management insights for managers to enhance the reliability are provided., We study reliability and sensitivity analysis of a retrial machine repair problem with working breakdowns operating under the F-policy. The F-policy studies the most common issue of controlling arrival to a queueing problem and it requires startup time before allowing failed machines to enter the orbit. The server is subject to working breakdowns only when there is at least one failed machine in the system. When the server is busy it works at a fast rate, but when it is subject to working breakdowns, it works at a slow rate. Failure and repair times of the server are exponentially distributed. The Laplace transform technique is utilized to develop two system performance measures such as system reliability and mean time to system failure (MTTF). Extensive numerical results are carried out to reveal how performance measures are affected by changing various values of each system parameter.

In this study, COPAM (Combined Optimization and Performance Analysis Model) software revealing optimum design possibilities and performance analysis of pressurized irrigation systems, was applied to on-demand pressurized irrigation system in Uludag University Agricultural Application and Research Centre, Bursa, Turkey. The system reliability, hydrant pressure heads, upstream elevation, discharges and pipe diameters related to this irrigation system were analyzed with COPAM software which have a variety of analysis tools. Analysis results showed that there were no deficiencies of performance in the hydrant level of the examined system. Furthermore, pipe diameters of the existing irrigation network were recalculated.

This paper investigates Bayesian methods for aerospace system reliability analysis using various sources of test data and expert knowledge at both subsystem and system levels. Four scenarios based on available information for the priors and test data of a system and/or subsystems are studied using specific Bayesian inference techniques. This paper proposes the Bayesian melding method for integrating subsystem-level priors with system-level priors for both system- and subsystem-level reliability analysis. System and subsystem reliability outcomes are compared under different scenarios. Computational challenges for posterior inferences using the sophisticated Bayesian melding method are addressed using Markov Chain Monte Carlo (MCMC) and adaptive Sampling Importance Re-sampling (SIR) methods. A case study with simulation results illustrates the applications of the proposed methods and provides insights for aerospace system reliability analysis using available multilevel information.

Motivated by practical engineering requirement, we propose a Bayesian-based approach mainly addressing the reliability estimation of the system under the imperfect situation. In particular, a novel Bayesian melding approach is developed in the presence of inconsistent priors. Limitations that embedded in traditional melding approach have been eliminated by setting the weighing factor as a hyper parameter. We implement our proposal via a modified sampling importance resampling algorithm, which is developed to balance the contributions of two different probability distributions in a flexible way. Both numerical case and practical engineering application example are demonstrated for validation and benefit illustration.

In this day and age Voice over Internet Protocol (VoIP) has turned out to be a revolutionary technology in the field of telecommunication. Because of the escalating number of users of VoIP, it is essential to intend for a reliable VoIP system providing good quality of service. In this paper a stochastic model based on continuous time Markov chain is developed to analyze the reliability of a server based VoIP system comprising of significant components only. The concurrent breakdown of more than one component is taken into modeling concern. Redundancy at the component level is used with the purpose to boost the system reliability of VoIP. As a defensive course of action, software rejuvenation is implemented to avert or suspend software failures. And an optimal software rejuvenation strategy is proposed which leads to increased system reliability. Numerical results are presented for the quantitative examination of the suggested model.

The purpose of this paper is to highlight that, in order to assess the availability of different HVDC cable transmission systems, a more detailed characterization of the cable management significantly affects the availability estimation since the cable represents one of the most critical elements of such systems. The analyzed case study consists of a multi-terminal direct current system based on both line commutated converter and voltage source converter technologies in different configurations, whose availability is computed for different transmitted power capacities. For these analyses, the matrix-based reliability estimation method is exploited together with the Monte Carlo approach and the Markov state space one. This paper shows how reliability analysis requires a deep knowledge of the real installation conditions. The impact of these conditions on the reliability evaluation and the involved benefits are also presented.

The basic purpose of an electric power system is to supply its consumers with electric energy as parsimoniously as possible and with a sensible degree of continuity and quality. It is expected that the solicitation of power system reliability assessment in bulk power systems will continue to increase in the future especially in the newly deregulated power diligence. This paper presents the research conducted on the three areas of incorporating multi-state generating unit models, evaluating system performance indices and identifying transmission paucities in complex system adequacy assessment. The incentives for electricity market participants to endow in new generation and transmission facilities are highly influenced by the market risk in a complex restructured environment. This paper also presents a procedure to identify transmission deficiencies and remedial modification in the composite generation and transmission system and focused on the application of probabilistic techniques in composite system adequacy assessment

As of late, expanding interest of renewable energy and consumption of non-renewable energy source have prompted developing advancement of renewable energy technology, for example, wind energy. Wind energy has turned out to be one of the reliable sources of renewable energy, which requests extra transmission capacity and better methods for sustaining system reliability. As of now, doubly fed induction generator wind turbine is the most well-known wind turbine. This paper focuses on DFIG wind farm design using MATLAB/SIMULINK and also investigates the issues of the system stability of the DFIG wind turbine micro grid power system. This analysis includes the changes of voltage, current, real power and reactive power based on various conditions of the power system.

With the increase in system complexity, the intelligent heuristic optimization methods have received more and more attention on system reliability analysis. However, the objective functions and constraint conditions of system reliability are nonlinear. Thereby, a hybrid optimization method was proposed, based on the shuffled frog leaping algorithm and bacterial foraging algorithm, to solve the problem of system reliability and redundancy allocation. First, random grouping strategy was added to maintain the diversity of the population. Then, the Levy flight update strategy was used to increase the global search ability. Finally, the method of migration operation was introduced to escape from local optimums. The proposed methodology, a new version of the SFLA algorithm, was then applied to the mathematical test and the operation of the system reliability model, respectively. Results show that compared to the common methods, it can obtain the best solution, with the maximum value of the system reliability.

Historically, people in flood-prone areas world-wide have (to a certain degree) accepted the risk of being flooded because of the benefits that flood-prone areas can provide; examples of such benefits are rich agricultural lands or trade advantages. Acceptance and benefits notwithstanding, people living in flood-prone areas have tried, and will continue to try, to reduce and manage their vulnerability and degree of exposure to floods. A typical risk reduction measure is building flood defences...

The adoption of composites as aircraft primary structural material has created a real need for a new aircraft maintenance philosophy to enable almost real-time structural condition assessment. It is thus crucial to develop structural health monitoring (SHM) systems capable of performing damage diagnostic and remaining useful life prognostic. At the same time, the urge to reduce production costs has led to consistent developments in thermoplastic composite (TpC) technology. In particular, new possibilities have been unlocked for automated assembly processes based on welding. This context constitutes a unique opportunity for integrating research on SHM into the advances of TpC technology, in order to contribute to a combined reduction of production and maintenance costs, and thus to the development of a truly cost-effective composite airframe. Over the last three decades, ultrasonic guided waves (GWs) have been recognised as having a great potential for detailed quantitative diagnostic of damage in composite structures. However, there are still no certified GW based SHM (GW-SHM) applications for civil aircraft. The reason for that is a limited understanding about the interaction between measurement variability factors associated with real operational environments, damage types, materials and geometric complexity. Therefore, the aim of the research presented in this thesis was to accelerate the bridging of those knowledge gaps and thereby to improve the reliability of GW-SHM for composite aircraft.The research presented in this thesis has put forward three possible paths for improving the reliability of GW-SHM systems for composite aircraft. First, to the early detection of manufacturing defects by investigating the relationship between GW propagation and assembly process parameters at structural element scale. Second, to reduce the uncertainty in the damage diagnostic by increasing the systematisation of the GW-SHM system design. Third, to increase the robustness of damage diagnostic capabilities by studying the effects of real operational-environmental variability factors on GW propagation at real scale.

The system-based design approach for steel structures using advanced structural analysis, referred to as the Direct Design Method (DDM), has gained much research interest from the scientific community, and has been permitted in a number of steel structural specifications such as American specification AISC360 and Australian standard AS4100. Unfortunately, even the most sophisticated nonlinear analysis method cannot predict the actual performance of a structure with certainty due to ever-present uncertainties in the demands imposed to the structure and structural resistance. To fulfill the goal of assuring a prescribed level of structural reliability in the DDM, a resistance factor applied at the system level (system resistance factor) is used in the DDM. The system resistance factor needs to be determined based on a system reliability calibration procedure. This thesis examines the role of connection behaviors and the associated uncertainties on the system reliabilities of semi-rigid frames designed by the DDM. Probabilistic models of connection properties (connection stiffness and strength) are established for five types of commonly used connections based on the connection test data compiled from the literature, including the single web angle connection, double web angle connection, top and seat angle connection (with or without webangle), and extended end plate connection. The popular three-parameter-power model is used to compute the “nominal” moment-rotation response of the connections, to normalize the test data. By comparing the test-to-nominal results, the mean-to-nominal ratio and coefficient of variation of connection stiffness/strength are estimated, and incorporated in the system reliability analyses. Six series of typical planar steel frames with partially restrained beam-column connections are selected for conducting system reliability calibrations for the DDM. For each model frame, different types of connections with different stiffness and strength are considered, including the extended end plate connections, top and seat withe double web angle connections, and double web angle connections. First, the design strength of the frame given by the DDM and AISC LRFD are compared. Then, system reliability evaluations are performed for two most common design load combinations, i.e., combined wind and gravity loads, and gravity load only combination. Different loading scenarios are considered (wind-to-gravity load ratio, live-to-dead load ratio). The effects of connection type, connection stiffness and strength on the system reliability and system resistance factor are investigated. The relationship between the system resistance factor and frame reliability is established, which can serve as a basis for the code writers to choose the appropriate system resistance factors for the DDM of semi-rigid frames.

Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

The conception (or risk assessment) of complex mechanical systems has to take into account a series of constraints. Such constraints can be due to certification criteria, performance objectives, cost limitations, and so on. In this context, the role of simulation has kept increasing for the last decades, as it should be able to predict if a given configuration of the system is likely to fulfill these constraints without having to build it and to test it experimentally. In many cases, the computation of these constraints is associated with a series of computer software, whose physics can vary a lot. For instance, in the car industry, the conception of a new vehicle can be subjected to constraints on its size and weight, which are rather easy to compute, but also on its emergency stopping distance, its crash or aerodynamic resistance, which can be much more difficult to evaluate. Thus, several software (structure dynamics, multibody modeling, fluid dynamics software for instance) are generally needed to verify all the constraints. Numerical methods to identify the limits of such “conception domains”, that is to say domains in which all the constraints are verified, are needed [1]. As the computational costs associated with the constraints can be very different, the question then arises of how to optimize the number of evaluations of each code to minimize the uncertainties about these limits, for a given computational budget. In this prospect, this paper presents an innovative approach to predict the limits of the considered conception domain, but also to quantify the uncertainty associated with this prediction. Based on an adaptive Gaussian process regression, this method allows us to find iteratively the new code evaluations that will maximize the knowledge about the searched limits at the minimal computational cost. First, the scientific basis of such an approach will be presented. The efficiency of such a method will then be illustrated on an analytical example.

Modern data centers have to rely on continuous power to provide reliable and trustable services to their end users. Such power which comes from the main energy provider is most of the times supported by local uninterruptable power supply devices (UPS) which should therefore have a higher order of magnitude in terms of exploitability and reliability with respect to the hardware they are locally supporting. The reliability and availability performance of such systems must be therefore analyzed on both perspectives; the first one centered on the end user possibility to rely on a device every time it is needed and the second one aiming at a constant product performance improvement. These two different viewpoints can be far enough to lead to different product development strategies. In this manuscript a high efficiency modular UPS based on multicore control system for datacenter applications is presented and its reliability and availability figures discussed both from the designer and end user standpoints, under different operating conditions.