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

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

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]

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]

Anchor cables and piles are commonly adopted to improve the stability of slopes under seismic loading. System reliability analysis (SRA) is essential for assessing the stability of slopes stabilized with anchor cables and piles (SSCPs) due to the various uncertainties and complex interactions in geological and geotechnical engineering. A new SRA approach for SSCPs was proposed by considering the effect of the failure of local anchor cables and piles on the system. The failure event of the SSCP was decomposed into multiple failure events with different numbers and sequences of failed anchor cables and piles. The system failure probability (PSF) of the SSCP was calculated by summing the probabilities of all mutually exclusive failure events according to the total probability formulation. An example slope with weak layers in Yunnan Province, China, was employed to illustrate the proposed approach. The results show that this approach does not need to determine the correlation coefficients of multiple failure modes of SSCPs and is able to take into account the effect of the failure of the local anchor cables and piles on the SSCP system. The failure of the local anchor cables and piles significantly increases the failure probability of the remaining anchor cables, piles, and slopes due to stress redistribution, which may lead to cascading failures of anchor cables and piles failing one after another. The proposed approach provides a unique value for PSF rather than a range with upper and lower bounds, which is more precise and convenient for assessing the system reliability of SSCPs. The failure probability of SSCPs could be underestimated if the failure of local anchor cables and piles is ignored. [ABSTRACT FROM AUTHOR]

Prestressed girders often deteriorate over time due to environmental and man-made stressors, lowering the strength and serviceability of bridge structures. Although structural repairs are implemented to improve the load carrying capacity of the structure, the presence of numerous unknowns leads to high uncertainty in estimating the adequacy of repairs. For instance, the cross-section of the remaining strands, material properties, applied external loads, and workmanship assumptions made throughout the repair process introduce ambiguities when estimating the adequacy of the repairs. This study evaluates the efficiency of re-tensioning repairs of prestressed concrete bridge span girders. The repairs include field splicing, re-tensioning, of deteriorated or damaged strands by torquing a splicing coupler. The evaluation in this study considers component, system reliability, and load ratings while accounting for several uncertainties, such as structural repair, material properties, and external loads. This paper introduces an approach to account for prestressing strands damage and repair uncertainties while also accounting for other uncertainties. In this regard, five cases are studied: as-built, repaired, and three varying degrees of damage cases. First, the distribution for structural demand and capacity accounting for uncertainty in loads, material properties, and repair process is defined for each girder in the prestressed concrete bridge span. In doing so, Monte-Carlo simulation is employed to determine the distributions. Accordingly, the limit state function of the girders is defined from the obtained distributions. Then, the component reliability of each AASHTO (American Association of State Highway and Transportation Officials) Type II girder is calculated from the obtained reliability indices based on the determined limit state functions. Finally, a system reliability model of the span is developed from the component reliability of each girder. Some advantages and disadvantages of using component and system reliability index versus load rating in damaged and repaired prestressed concrete bridge girders are also discussed. Several critical conclusions are made regarding the uncertainties in structural repair, material properties and external loads, and their impact on the load rating and the component and system reliability of the prestressed concrete bridge structure girders. [ABSTRACT FROM AUTHOR]

Reliability analysis for complex systems is a challenging problem, because of complex failure regions and frequently time-consuming simulations. Especially for complex systems with extremely rare events, it is of great significance to evaluate the reliability efficiently and accurately. Therefore, a novel reliability analysis method that combines the dependent Kriging method and adaptive spherical decomposition sampling for the rare event is proposed in this work to solve these problems. It makes full use of the mean and variance information of the Kriging predicted responses and the covariance information between the responses. In addition, the stopping criterion is directly related to the accuracy of failure probability rather than the accuracy of model construction. Furthermore, spherical decomposition sampling is used to estimate the small failure probability and improve sampling efficiency. Three test examples are presented to illustrate the accuracy and efficiency of the proposed method. Meanwhile, the flap motion mechanism is used as the research object to verify the application of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a strategic level of a supply chain is studied. To deal with its objective, restructuring distribution and manufacturing centers under the parameter uncertainty and system disruption are formulated as a mixed-integer nonlinear programming (MINLP) model. Because of the complexity of such a hard model, it is converted to a mixed-integer linear programming (MILP) model by conducting a simple linearization method. Capacity expansion and reduction, capacity consolidation, outsourcing, and transshipment strategies are used to reconfigure the considered supply chain. An inexact interval fixed-mix fuzzy approach is applied to deal with the parameter uncertainty, and an efficient two-stage model is conducted to analyze the system reliability. The developed model is solved by an efficient and effective Lagrangian relaxation procedure. Furthermore, a case study of dairy products is studied to present the model application and validation. Finally, several sensitivity analyses are carried out to identify the model behavior and solution algorithm validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Several applications of darkroom is observed in modern technological applications. A darkroom can be made completely dark to allow the processing of the light-sensitive photographic materials, including film and photographic paper. Various equipments are used in the darkroom, including an enlarger, baths containing chemicals and running water. It is used to process photographic film, to make prints and to carry out other associated tasks. Also, in non-destructive testing such as magnetic particle inspection a darkroom is essential. Here, we studied the reliability of the illumination of a darkroom under different scenarios of the lighting systems. We have developed three models for illuminating the darkroom with a single operating switch with two tube lights, a single operating switch with multiple tube lights and multiple switches operating individual tube light. We have designed the simple unconstrained model and also the constrained redundancy allocation models. Moreover, we have considered the uncertainty models in the form of intuitionistic fuzzy number as it is the more generalization of the fuzzy numbers. In these imprecise models, the parameters along with the component reliabilities are taken as triangular intuitionistic fuzzy numbers. Before solving these imprecise models, we have crispified these into deterministic form by using suitable and efficient crispification method (BADD). The constrained redundancy allocation problems of integer programming type are obtained in each model and we have implemented evolutionary algorithm WQPSO to solve these problems successfully. Result analysis with comparison has been presented with the recommendation that the third model is more reliable in illuminating the darkroom. [ABSTRACT FROM AUTHOR]

Conventional interview surveys for assessing customer satisfaction require supplementary resources and yield a low response rate. Covering fewer customers who generally face service interruptions is another limitation that hinders the identification of the root causes of failure. The survey findings might mislead the utilities by showing unrealistic satisfaction levels. Customer dissatisfaction risk subjects to the time between the complaint registration and the utility response pending full resolution of the issue. In the present research, bowtie analysis (BTA) reveals an innovative synergetic correlation between system reliability and customer satisfaction with water utilities. Using complaints reported due to service failures, the fault tree analysis (FTA) assesses the reliability of system components (pressure, water quality, and structure). On the opposite side of the bowtie, event tree analysis (ETA) evaluates the efficiency of the utility response and translates it into customer dissatisfaction risk. Exploring over 1500 registered work orders in a utility operating in British Columbia (Canada) reveals that 80% of complaints originated from structural (primarily service connections) issues. Simulating the BTA for five improvements showed that economical proactive maintenance and an efficient complaint response mechanism improve the system reliability from 58 to 86% and reduce the cumulative risk by 62%. The remaining risk reduction requires expensive improvements, e.g., source change and conventional treatment installation, which require customer approval based on willingness to pay. The study reveals that a complete bowtie analysis effectively integrates both the FTA and ETA and provides a novel customer-driven decision-support tool for water supply systems and other elsewhere applicable. [ABSTRACT FROM AUTHOR]

Reservoir-induced landslides are a common type of geological hazards in the hydropower area, where the frequent occurrence of catastrophic landslides has attracted widespread attention. This study focuses on the Huangtupo Riverside Slump 1#, a reservoir-induced landslide in the Three Gorges Reservoir Area of China, which has been investigated with double sliding surfaces. First, a series of numerical analyses are conducted to obtain the seepage field inside the landslide subjected to fluctuation of reservoir water level. Then the stability of the landslide is analyzed by employing the results of the seepage field. Subsequently, considering the uncertainty of strength parameters of sliding surfaces, the failure probabilities for the individual sliding surfaces are assessed via first order reliability method, and then the system reliability of the landslide is analyzed by applying the Ditlevsen’s bounds method. The results indicate that the change of seepage field is closely related to the scheduling of reservoir water level and lags behind the scheduling, which has a greater impact on the stability of the 1–1# failure surface than the 1–2# failure surface. Similarly, the bounds of system failure probability vary with the reservoir water level, and the most dangerous probability occurs when the water drops to the lowest level. Evaluating the landslide from the perspective of system reliability greatly narrows the risk range, which has potential for further application in risk management and control. Besides, the relationships between the correlation coefficient of strength parameters and the landslide system response are discussed through parametric analyses. [ABSTRACT FROM AUTHOR]

In this paper, a modern computer network, cloud-based network, which comprises internet of things (IoT), edge servers, and cloud servers for data transmission, is investigated and evaluated. A cloud-based network is modeled as a graph having a set of nodes and a set of links. Each link represents a transmission route, and each node represents a device, such as an IoT device, edge server, and cloud server. In practical, a transmission route comprises several physical lines or virtual channels. Each physical line (virtual channel) may provide a capacity or may fail to imply several and stochastic states. Such a cloud-based network is called a stochastic flow cloud-based network (SCN) herein. System reliability for an SCN is then evaluated. It is defined as the probability of the data being successfully transmitted through the SCN under edge server capacity and budget constraints. The SCN is modeled firstly in order to elucidate the flow relationship among the whole system; capacity limitation of the edge servers and costs of data transmission/process are also considered. Subsequently, we conclude an algorithm to evaluate system reliability. Supervisors can manage the SCN based on system reliability which presents the system capability with capacity and budget consideration. [ABSTRACT FROM AUTHOR]

We describe the notion of stability of coherent systems as a framework to deal with redundancy. We define stable coherent systems and show how this notion can help the design of reliable systems. We demonstrate that the reliability of stable systems can be efficiently computed using the algebraic versions of improved inclusion-exclusion formulas and sum of disjoint products. [ABSTRACT FROM AUTHOR]

Correlation can seriously degrade reliability and capacity due to the simultaneous failure of multiple components, which lowers the probability that a system can execute its required functions with acceptable levels of confidence. The high cost of fault in time-critical systems necessitates methods to explicitly consider the influence of correlation on reliability. This paper constructs a network-structured model, namely time-constrained multi-state network (TCMSN), to investigate the capacity of a computer network. In the TCMSN, the physical lines comprising the edges of the computer network experience correlated faults. Our approach quantifies the probability that d units of data can be sent from source to sink in no more than T units of time. This probability that the computer network delivers a specified level of data before the deadline is referred to as the system reliability. Experimental results indicate that the negative influence of correlation on reliability could be significant, especially when the data amount is close to network bandwidth and the time constraint is tight. The modeling approach will subsequently promote design and optimization studies to mitigate the vulnerability of networks to correlated faults. [ABSTRACT FROM AUTHOR]

To achieve the most efficiency in supply chain management, the capability of distribution networks is a key point for the entire supply chain. Stocks are critical for enhancing the efficiency of satisfying the demand of retailers in the distribution network. A configuration of a distribution network is consisted of routes and nodes. Each route connects a pair of nodes and each node is denoted as a supplier, a distribution center, or a retailer. For each route, it has a carrier whose available capacity for demand transmission is multi state. Hence, a distribution network is also regarded as a multi state network and such a network is named as a multi state distribution network (MDN) in here. The propose of this paper is to evaluate the system reliability which is defined as the probability that the MDN can meet all retailers' demand under stocks. In practical, all retailers' demand should be satisfied by stocks in the distribution centers (DCs) firstly. Therefore flow assignment in MDN model is mainly clarified by the relationship between the demand of retailers, stocks on DCs, and suppliers. The concept of minimal capacity vectors (MCVs) is then proposed and an algorithm is developed to obtaining MCVs for evaluating system reliability. [ABSTRACT FROM AUTHOR]

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs. [ABSTRACT FROM AUTHOR]

Although the shear strength reduction method (SRM) tends to give a more realistic prediction of a slope's failure mechanism and of its associated factor of safety (FS) than the limit equilibrium method (LEM), it has had few applications in system reliability analyses of soil slopes due to its high computational cost. Active-learning surrogate model–based approaches provide a rational way to alleviate such computation burden through significantly reducing the required number of deterministic analyses (i.e., evaluation of FS). However, such approaches have rare combinations with SRM for slope reliability analyses. In this paper, improved versions of three mainstream active-learning surrogate models (ASMs)—active-learning support vector machine (ASVM), active-learning radial basis function (ARBF), and active-learning Kriging (AK)—are employed to perform a comparative study considering their model efficiency and model stability, by which the ASM shown optimal combinatorial properties with SRM, can be singled out. During the reliability analyses, Monte Carlo simulation (MCS) is employed with the ASMs to estimate the system probability of failure (Pf,s) of soil slopes. Three soil slopes taken from the literature are employed to assess the performance of the suggested procedures. Results show that the introduced ASMs provide good estimates of Pf,s for soil slopes with different numbers of layers and of random variables, and that they often outperform other alternative methods, particularly for layered soil slopes. Among the ASMs considered, the ARBF shows the best performance in both computational efficiency and model stability. [ABSTRACT FROM AUTHOR]

In this article, we consider the maximum likelihood estimation (MLE) of the system reliability for the exponential distribution. We propose the estimation of the system reliability based on moving extreme and MiniMax ranked set sampling mechanisms. Since the proposed MLE estimators of cannot be obtained in a closed form, we apply Mehrotra and Nanda's modified MLE methodology. The performance of the suggested estimators is compared with their competitors based on simple random sample by Monte Carlo simulations under both perfect and imperfect ranking assumptions. Real data from the medical field is analyzed to show the applicability of the proposed estimators. [ABSTRACT FROM AUTHOR]

Due to the current financial constraints in the energy industry, investors need realistic risk evaluation and economic analysis in energy investment projects. On the other hand, energy system operators are continuously facing a wide range of challenges when dealing with asset management practices of the system components. In this paper, a novel method is proposed for the availability assessment of energy systems. This method provides an opportunity to predict system real behavior patterns in simulated time. According to the designed procedure, two new parameters for availability assessment are defined as the Failure Moment (FM) and the Repair Moment (RM). FM and RM of the components are estimated by the Monte Carlo method based on the time-varying failure rate and repair rate, respectively. This process is continued for all selected components in the system lifetime. As a common practice for demonstrating novel methodologies, the proposed method of this study is applied to the Combined Cycle Gas Turbine Power Plants. The results show that realistic availability evaluation for energy systems with the Monte Carlo method can cause a 4.4% reduction in nominal fuel consumption (16.79 kg/s as an average fuel flow rate vs. 17.562 kg/s as nominal amount) and a 5.05% reduction in nominal power generation. Therefore, this consideration makes the investment analysis more accurate and the decision to invest more interesting. [ABSTRACT FROM AUTHOR]

Reliability analysis provides a more effective method for slope stability evaluations and design because it can reflect the uncertainties of slopes. In this study, a generalized probability density evolution method (GPDEM) is proposed to evaluate the system stability reliability of multi-parameters complex slopes. This method is completely different with the traditional random reliability analytical method, which can combine a set of stochastic analysis and currently advanced deterministic slope stability analysis method using finite element method. The proposed method has great adaptability to complex slope cases with implicit perform functions and multiple parametric variables, and can directly reflect the reliability. In addition, a newly Galerkin finite element method based on Streamline-Upwind/Petrov–Galerkin (SUPG) scheme is proposed for the numerical solution of the GPDEM to increase the convergence rates. Several representative study cases of slope system reliability analysis are selected to certify the high precision and validity of GPDEM over the Monte Carlo simulation (MCS) and other simulation methods. The results demonstrate the novel method is promising for geotechnical engineering in terms of precision and efficiency, and can be used to analyze the failure probability of large and complex geotechnical engineering structures. [ABSTRACT FROM AUTHOR]

The existing engineering structural system is featured by complicated limit state functions of high nonlinearity, resulting in the conventional reliability analysis approach that is unable to evaluate these problems accurately or even unavailable. Various analytical methods have been presented by researchers over the past decades. However, these methods have difficulties in striking a balance between accuracy and computational efficiency. This paper proposes a relatively efficient and accurate procedure to estimate the failure probability of the parallel system based on the FORM and a new metaheuristic algorithm, chaotic enhanced colliding bodies optimization algorithm (CECBO). The CECBO has shown robustness, high accuracy, and implementation simplicity for solving optimization problems from previous work. Thus, the CECBO is employed to search the point of maximum likelihood (PML), a point that yields the highest value of the probability density function in the failure domain, through solving an optimization model and then approximates the failure domain of the parallel system to estimate the failure probability accurately. The results of the test examples indicate that the proposed procedure is promising for the estimation of the failure probability of a parallel system in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

A multistate flow network (MSFN) is an applicable model for constructing and evaluating practical systems, including an information system in resilient supply chain network. When assessing the performance of an MSFN, system reliability is a critical indicator in determining the probability that a given demand will be transmitted by the MSFN. This study develops a minimal cut (MC)-based simulation approach for estimating the system reliability of an MSFN. This simulation approach does not require the upper boundary points for the demand, which are essential in many existing simulation approaches. Additionally, the concept of a cut size limit is integrated into the simulation to reduce the number of MCs, with the computational time exhibiting a linear dependence on the number of MCs. The novel MC-reduction simulation approach proposed in this study estimates system reliability more efficiently than traditional approaches. The effectiveness of the MC-reduction simulation approach is validated on a series of examples, including a time-series analysis of a real-life computer system. Therefore, the proposed approach is advantaged to evaluate the performance of information system in a large supply chain network. [ABSTRACT FROM AUTHOR]

As a slope may have numerous potential slip surfaces, its failure probability as a system is usually different from that of a single slip surface. Nevertheless, the system failure probability of a slope is often governed by several representative slip surfaces. How to identify the representative slip surfaces is important for system reliability analysis of soil slopes. Previous studies on representative slip surface identification mainly focus on circular slip surfaces within the framework of limit equilibrium methods; there are also relevant researches, albeit limited, within the framework of finite element/finite difference analyses based on the shear strength reduction method. By viewing the task of representative slip surface identification as a multidesign point identification problem, a barrier-based optimization method is suggested in this paper to identify representative slip surfaces of arbitrary shape based on the shear strength reduction method. A multiple starting point strategy is suggested to enhance its efficiency. For the three slopes examined in this paper, the method suggested is capable of identifying representative slip surfaces efficiently without prior assumption on their shapes. [ABSTRACT FROM AUTHOR]

At present, there are two problems in 3D slope reliability analysis: (1) the slope stability analysis method; (2) how to effectively extend the reliability analysis method from 2D to 3D slopes. This paper combines the advantages of the finite element method and the limit equilibrium method, develops a 3D slope stability analysis finite element sliding surface stress method program, and introduces the radial basis function network (RBFN) intelligent response surface method, which has the characteristics of strong adaptability, high fault tolerance, greater flexibility, and strong nonlinearity. With the response surface function built for the general framework for an intelligent response surface methodology for the reliability analysis of a 3D slope system, the reliability analysis of a slope is extended from 2D to 3D. The process involves generating samples, creating an intelligent response surface, and calculating the failure probability of the 3D slope system. Through the reliability analysis of typical examples and a comparison with Monte Carlo simulation (MCS) method results, the accuracy, feasibility, and superiority of the proposed intelligent response surface methodology for application to the reliability analysis of a 3D slope system were verified. A comparison with the analysis results of the 3D slope shows that the 2D slope stability calculation is too conservative and seriously overestimates the probability of slope instability. Furthermore, 3D analysis can consider the true stress state of a slope and is able to specify a definite range for a sliding body, thereby providing a basis and reference for determining reinforcement regions. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic approach to reveal the negative consequences of overlooking code calibration when adopting the provisions of nonindigenous building codes in design. A taxonomy of the national building codes of various countries whose provisions are adopted from one or more original standards is first presented. Next, the reliability-based approach to quantify the lack of safety that may arise due to the absence of code calibration is presented. This approach is then applied to the case study of the National Building Code of Iran, whose load and resistance factors are directly adopted from ASCE/SEI 7. To this end, seven steel building archetypes with various structural systems at various regions with different seismicity levels and site classes are designed according to the Iranian loading and steel design codes. Subsequently, 2510 structural elements, including beams, columns, and braces, from these archetypes are subjected to reliability analysis to compute their reliabilities. In this analysis, the uncertainty of material properties are quantified using local laboratory test data and the uncertainties associated with manufacturing tolerances of steel elements are quantified through extensive field surveys. The analysis results in reliability indices that are markedly smaller than the targets set by the original standards the Iranian code has adopted from, which exposes the notable lack of safety in the ensuing designs. Subsequently, the load and resistance factors are calibrated to achieve the target reliabilities. Among other changes, the results indicate the need for a 25% increase in the earthquake load factor, which reveals the lack of safety that is implicit in the current practice. Reliability sensitivity analysis is then employed to determine the sources of uncertainty that are most influential on the resulting reliabilities. The presented approach is immediately applicable to the assessment of the building codes of other countries. [ABSTRACT FROM AUTHOR]

Resource allocation and their scheduling to optimize performance measures in heterogeneous environments are famous such as an NP-hard issue, not only for the resource heterogeneity, but also for the possibility of applying allocation to take advantage of idle resource. This article proposes a scheduling technique for communicating tasks by using two hybrid genetic algorithms (HGAs) which minimizes system cost and response time and maximizes the reliability of the distributed real time system. In the present technique, convergence of genetic algorithm (GA) is made better by offering new encoding and population initialization method and genetic operations. This technique is completed in two phases: Phase I develops hybrid c-mean genetic algorithm (HCMGA) which is a fusion of fuzzy c-means (FCM) technique and genetic algorithm (GA) and Phase II develops hybrid branch and bound genetic algorithm (HBBGA) which is a fusion of branch and bound (B&B) technique and genetic algorithm (GA). HCMGA, makes 's' clusters of 'r' tasks by using FCM clustering technique then these clusters are updated by using GA to get final clusters of tasks. HBBGA, initially allocates clusters of tasks onto processors by B&B technique then their allocations are updated by using GA to get the final allocation. To check the performance of the proposed technique, several examples are considered from different research articles and results of the numerical examples have compared with well-regard existing models. The proposed technique is able to outperform all comparative techniques established in the literature; thus, superior results are obtained. This technique is suitable for arbitrary number of processors and tasks. [ABSTRACT FROM AUTHOR]

This study first presents accuracy assessments of two default models proposed in two popular soil nail wall design specifications and their calibrated versions for estimation of maximum soil nail loads under operational conditions. The assessments are based on a general nail load database reported in the literature. Evaluation results showed that predictions using the default and calibrated models are unsatisfactory as the dispersions are high to extremely high. Simple calibration terms are introduced to the calibrated models for performance enhancement. The recalibrated models are shown to be accurate on average, and the prediction dispersions are significantly reduced. The model biases for the above six models are demonstrated to be lognormal random variables. Individual reliability-based designs for the nail pullout and nail-in-tension limit states using the six models are carried out. System reliability of nails against internal failures is also explored. The correlation between the two limit states is investigated, and its influences on system reliability evaluation are discussed. The practical value of model calibration is demonstrated as using the recalibrated models for nail designs leads to the most cost-effective design outcomes. This study represents a solid step toward development of reliability-based design framework for soil nail walls. [ABSTRACT FROM AUTHOR]

Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the exponential Monte Carlo with counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results show that Q-EMCQ is an efficient technique for test case generation. Addition- ally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyper-heuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time. [ABSTRACT FROM AUTHOR]

To describe the system fault evolution process, the space fault network theory is proposed. Space fault network theory is the third stage of space fault tree theory. The paper introduces the basic research results of space fault network. The basic ideas, basic definitions and corresponding physical meanings of space fault network are discussed. The properties, structure and space fault tree transformation method of space fault network are further studied. The characteristics of the system fault evolution process and its four elements are discussed. The representation methods of space fault network for system fault evolution process are presented. The transformation methods of the general space fault network and multidirectional ring space fault network into space fault tree are given and studied. In particular, the classification and characteristics of unidirectional ring space fault network are studied in detail. Based on the system fault evolution process of the example, we conducted a qualitative analysis, and a space fault network is established and transformed. At the same time, a quantitative analysis is carried out according to the derivation process of the transformation of multidirectional ring space fault network. The results show that the space fault network can effectively describe and analyze the system fault evolution process. This paper has solved some basic problems of system fault evolution process, but more complicated situations need further research. [ABSTRACT FROM AUTHOR]

This study investigates strategies for solving the system reliability of large three-dimensional jacket structures. These structural systems normally fail as a result of a series of different components failures. The failure characteristics are investigated under various environmental conditions and direction combinations. The β-unzipping technique is adopted to determine critical failure components, and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability. However, this approach needs excessive computational effort for searching failure components and failure paths. Based on a trained artificial neural network (ANN), which can be used to approximate the implicit limit-state function of a complicated structure, a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method. The failure probability is calculated through Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS). The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield, Bohai Sea, China. This study provides a reference for the evaluation of the system reliability of jacket structures. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is one of the typical structured, systematic and proactive approaches for product or system failure analysis. A critical step in FMEA is identifying potential failure modes for product sub-systems, components, and processes, for which component-failure mode (CF) knowledge is necessarily needed as an important source of knowledge. However, this knowledge is usually acquired manually based on historical documents such as bills of material and failure analysis reports, which is a labor-intensive and time-consuming task, incurring inefficiency and plenty of mistakes. Nevertheless, few existing studies have developed an effective and intelligent approach to acquiring accurate CF knowledge automatically. To fill the gap, this paper proposes a method to construct the CF matrix automatically by mining unstructured and short quality problem texts and mapping as well as representing them as CF knowledge. Starting with mining the frequent itemsets of failure modes through Apriori algorithm, the method uses the semantic dictionary WordNet to find synonyms in the set of failure modes, based on which the standard set of failure modes is finally built. Subsequently, upon the previous work and components set, we design the component-failure mode matrix mining (CFMM) algorithm and apply it to establish the CF matrix from unstructured quality problem texts. Lastly, we examine the quality data of the seat module of an automobile company as a case study in order to validate the proposed method. The result shows that the failure mode extraction method with standardized features can extract failure modes more effectively than the FP-growth and K-means clustering methods. Meanwhile, the devised CFMM algorithm can extract more combinations of CF than the FP-growth method and build a richer CF matrix. Although different industries have distinct domain characteristics, our proposed method can be applicable not only to manufacturing but also to other fields needing FMEA to enhance product and system reliability. [ABSTRACT FROM AUTHOR]

A major hurdle in the application of reliability-based design optimization (RBDO) to time-dependent systems is the continual interplay between calculating time-variant reliability (to ensure reliability policies are met) and moving the design point to optimize some objective function, such as cost, weight, size and so forth. In most cases the reliability can be obtained readily using so-called fast integration methods. However, this option is not available when certain stochastic processes are invoked to model gradual damage or deterioration. In this case, sampling methods must be used. This paper provides a novel way to obviate this inefficiency. First, a meta-model is built to relate time-variant system reliability to the entire design space (and noise space if required). A design of experiments paradigm and Monte Carlo simulation using the mechanistic model determines the corresponding system reliability accurately. A moving least-squares meta-model relates the data. Then, the optimization process to find the best design point, accesses the meta-model to quickly evaluate objectives and reliability constraints. Case-studies include a parallel Daniel's system and a series servo control system. The meta-model approach is simple, accurate and very fast, suggesting an attractive means for RBDO of time-dependent systems. [ABSTRACT FROM AUTHOR]

Practically, reliability-based system designs are modeled in various kinds of uncertainty such as expert's information character, qualitative statements, vagueness, etc. Fuzzy set theory is suitable for tackling such types of uncertainty effectively. In most of the practical situations, where reliability enhancement is an essential requirement, decision-making is a complicated task due to the presence of several mutually conflicting objectives such as system's cost, weight, and volume. To solve such problems, multi-objective evolutionary algorithms (MOEAs) are efficient techniques for finding multiple Pareto-optimal solutions in a single simulation run. This paper applies an elitist MOEA, namely, NSGA-II to fuzzy multi-objective reliability optimization problem consisting of conflicting objectives such as system reliability and its cost. Linguistic hedges (or modifiers) are used to modify the Pareto-optimal solution set obtained by NSGA-II in terms of the membership grades of the objective values. The max–min composition of the membership grades gives the maximum satisfaction level to each possible combination of the linguistic hedges. After that, fuzzy rule-based system (FRBS) is proposed for evaluating the system efficiency to each case which is used in the decision-making of reliability. A numerical example is given to illustrate the method. Finally, the proposed approach is comparatively studied with the existing approach. [ABSTRACT FROM AUTHOR]

Composite materials have been widely applied to engineering fields by virtue of its superior mechanical properties. Therefore, the problem of composite laminates safety assessment has also been discussed. This paper develops the non-probabilistic interval model-based system reliability evaluation strategy based on the last ply failure criterion (LPF), which improves the rough reliability solution based on first ply failure criterion (FPF) for composite laminates, and overcomes the shortage of traditional LPF method based on the probabilistic reliability theory. Obviously, the probability density function (PDF) is difficult to be calculated due to the experimental cost and time. In the proposed method, considering the fiber failure and matrix failure (limit state functions) for composite laminates, the failure possibility of every single ply is evaluated by the non-probabilistic interval model method. Subsequently, the progressive damage method is combined with the branch-bound method (B&B) to search the significant failure paths of composite laminates, and then the whole system analysis process is completed based on LPF criterion. Finally, the system reliability model of composite laminates can be constructed by introducing non-probabilistic interval model. Furthermore, the correlation description for the non-probabilistic interval model among is applied to the failure modes, and the Cornell first-order bound theory is applied to achieve the system reliability of composite laminates. After the detailed analysis steps, the numerical example of laminated plate is presented to demonstrate the validity and reasonability of the developed methodology. [ABSTRACT FROM AUTHOR]

For the fault tree analysis, a basic event probability is often complicated. The probability is not constant and even can be represented by function. In order to analyze the system reliability and related characteristics, we represent the probabilities of the basic events by functions. The variables of the function are n influencing factors on the basic events. We extend the top event probability from the constant value to n + 1-dimensional space considering n influencing factors, and the probability is n + 1th dimension. Further research the n + 1-dimensional space with related mathematical methods, and then, transform the system probability analysis into the problem of mathematic. The above ideas are the space fault tree (SFT). In SFT, component fault probability distribution replace basic event probability and system fault probability distribution replace top event probability. In this paper, we research the electrical system fault probability distribution and explain the related construction process. The main factors influencing the system are working temperature c and working time t. This paper constructs the three-dimensional fault probability distribution of the components and the system, and the probability importance and criticality importance of the components. With partial derivation of the system fault probability distribution by the c and t, we study the change trend of the fault probability. The optimal replacement schemes of components and the scheme considering the cost are obtained. The results show SFT is feasible and reasonable to analyze the fault probability of system under multi-factor influence and suitable for deep learning of the characteristics of the system reliability change. [ABSTRACT FROM AUTHOR]

With increasing application of Fused Deposition Modeling (FDM), it is important to develop a reliability model and maintenance plan strategies for FDM machines. This paper studies the impact of machine component degradations and process parameter deviations on the FDM system reliability. The contribution of this paper is to integrate both FDM machine reliability and process-dependent product quality in a system reliability model. The machine reliability is determined by component degradations. All component failures are subject to exponential distributions with time-dependent failure rates. The process-dependent product quality is used to quantify the impact of process parameters on product quality. The Flashforge Guider fabrication machine is used as a case study. The reliability model of this machine is analyzed during its operation time. Based on the reliability model, the optimal maintenance plans using multi-objective optimization method are obtained. The minimum life cycle cost and cumulative failure probability functions are taken as optimization objectives. Comparisons among the optimal maintenance plans and the solution without preventative maintenance (PM) are investigated. The implementation of PM improves the system reliability and reduces the life cycle cost. Through the Pareto frontiers, the trade-off is obtained between life cycle cost and cumulative failure probability functions. Numerical results for the optimization of FDM system operations are provided. [ABSTRACT FROM AUTHOR]

The primary concern of managers in logistics management is how market demand can be satisfied. The inventory in the transfer center of a distribution network plays a key role in meeting customer demands. This study focuses on evaluating the system reliability of a stochastic delivery-flow distribution network (SDDN) with inventory. An SDDN is composed of nodes and routes, where each node denotes a vendor, transfer center, or market and each route connects a pair of nodes. Along each route, there is a carrier whose available capacity is stochastic. Different from previous issues regarding system reliability, this study does not consider the vendor (source) and market (sink) but also the amount of stocks in transfer center is included. If the market demand cannot be met by inventory in transfer center, then vendors must meet the remaining demand to satisfy the market. Therefore an algorithm is developed in terms of minimal paths to evaluate system reliability, which is defined as the probability that the SDDN with stocks can satisfy the market demand from multiple vendors and transfer centers to the market under budget constraints. A numerical example is given to illustrate this proposed algorithm. [ABSTRACT FROM AUTHOR]

This study develops a novel Monte Carlo simulation (MCS) approach to estimate system reliability for a stochastic production system with finite buffer storage. System reliability indicates the probability of all workstations providing sufficient capacities to satisfy a specified demand, as well as that all buffer stations are not running out of storage. First, buffer stations are modeled in a stochastic production network (SPN) model and their storage usage is analyzed based on the network-structured SPN. Second, an MCS is developed to generate the system state and to check the storage usage of buffer stations to determine whether the demand can be satisfied. After repeated simulations, the system reliability of the SPN can be estimated. Experimental results show that the proposed MCS approach is effective and efficient in estimating system reliability with reasonable quality for an SPN within a reasonable time. More importantly, system reliability will be overestimated with infinite buffer storage, and thus, it is worth studying finite buffer storage. [ABSTRACT FROM AUTHOR]

The Space Fault Tree (SFT) is a theoretical and technical framework proposed by the author in 2012. The SFT measures the reliability with fault probability, and analyzes the relationship between system reliability and influencing factors. The characteristic function (CF) of SFT is constructed by the relationship between the component fault probability and the influencing factors, and is the basis of the SFT. The system fault data is different from the general monitoring data, which has large discreteness, randomness and fuzziness, that is, uncertainty. The existing characteristic function is a continuous function with finite discontinuities. The function can be considered as a kernel function of the fault data, but it is difficult to express the uncertainty of fault data. To this end, using cloud model (CM) to transform the characteristic function, so that it has the ability to express the uncertainty of data, called the cloudization characteristic function (CCF). The cloudization SFT (CLSFT) is constructed by using the CCF, and enables the relevant theory and methods of SFT to express the data uncertainty, thus the expression of the fault data characteristics and rules are more accurate. Firstly, the construction process and rationality analysis of CLSFT are given. Secondly, the concepts of SFT are reconstructed by cloud model. Use these definitions and methods to analyze the fault data of a simple electrical component. The relationship is studied between the component fault probability and the using time and using temperature. The results reflect the discreteness, randomness and fuzziness of the fault data to a large extent. In summary, the paper provides the reference for analyzing and controlling the uncertainty of the fault data and reliability in practical application. [ABSTRACT FROM AUTHOR]

In this paper, a class of electromagnetic field frequency domain reliability problem is first defined. The frequency domain reliability refers to the probability that an electromagnetic performance indicator can meet the intended requirements within a specific frequency band, considering the uncertainty of structural parameters and frequency-variant electromagnetic parameters. And then a frequency domain reliability analysis method based on univariate dimension reduction method is proposed, which provides an effective calculation tool for electromagnetic frequency domain reliability. In electromagnetic problems, performance indicators usually vary with frequency. The method firstly discretizes the frequency-variant performance indicator function into a series of frequency points' functions, and then transforms the frequency domain reliability problem into a series system reliability problem of discrete frequency points' functions. Secondly, the univariate dimension reduction method is introduced to solve the probability distribution functions and correlation coefficients of discrete frequency points' functions in the system. Finally, according to the above calculation results, the series system reliability can be solved to obtain the frequency domain reliability, and the cumulative distribution function of the performance indicator can also be obtained. In this study, Monte Carlo simulation is adopted to demonstrate the validity of the frequency domain reliability analysis method. Three examples are investigated to demonstrate the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Networks-on-chip (NoC) provide the communication infrastructure for high-speed and large-scale computation that integrates several IP-cores on a single die. Faults on network channels severely degrade system performance and throughput. This paper presents a distributed and online mechanism for detecting and locating stuck-at faults (SAFs) in NoC channels. We also study the effects of such faults on various network performance metrics. The inherent parallelism present in the architecture is utilized to design a scheduling scheme that reduces the overall test time and overhead significantly. The proposed test solution scales well with network size, channel width, and network topology. Hardware synthesis based on FPGA shows that it needs small area overhead and low test time compared to prior approaches. Furthermore, it improves packet latency and reduces energy consumption. [ABSTRACT FROM AUTHOR]

This paper discusses a new approach for system reliability parameter. Actually, we provide expectation Bayesian (E-Bayesian) estimation of system reliability for Series and parallel systems based on Pascal distribution. The definition and properties of E-Bayesian estimation are given. Also we applied three different distributions for the parameters in prior distribution to investigate the influence of the different prior distributions on the E-Bayesian estimation. The confidence intervals of R, based on E-Bayesian and bootstrap methods, are developed. The performance of these confidence intervals is studied through extensive simulation. Two numerical practical examples, is presented to illustrate the implementation of the proposed procedure. [ABSTRACT FROM AUTHOR]

In this paper, an improved time-variant reliability analysis method based on stochastic process discretization (iTRPD) is proposed. Firstly, the time-variant reliability problem is transformed into a time-invariant series system reliability problem. Then the first order reliability method (FORM) is employed to analyze the reliability of each component of the system, and a corresponding approach is given to calculate the correlation coefficient matrix of all the components’ performance functions. Finally, the target time-variant reliability can be obtained with the reliability index vector and the correlation coefficient matrix of the involved components. In this study, the iTRPD is further applied to the system reliability problems, and hence a corresponding time-variant system reliability analysis method is also developed. Four numerical examples are investigated to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Many mechanical systems have the characteristics of multiple failure modes and complex failure mechanisms. On the basis of stress-strength interference (SSI) model, this paper takes the mechanical system with common cause failure (CCF) as the research object. The relationship between the stress distribution and the strength distribution is studied, and the failures of components are independent of each other under the deterministic stress. Then, the concept of conditional reliability is introduced to build the system reliability models under the action of one-stress and multi-stress for both series and parallel systems. Finally, the corresponding properties of the proposed methods are discussed to show their advantages. [ABSTRACT FROM AUTHOR]

Modern products are complicated and standards of reliability are higher than ever before. Therefore, prediction of system reliability is essential, especially as the time available for developing products has decreased. Most of the previous studies assumed that the causes of failure are random. That is, the failure rates of components follow exponential distributions. To reflect various types of failure such as initial, random, or wear-out failure, we developed a novel procedure for estimating system reliability based on the Weibull distribution. We analyzed the effect of component selection on the types of system failure. Finally, we verified the proposed method by analyzing warranty data on actual Korean products. [ABSTRACT FROM AUTHOR]

The quasi-Newton approximation-based second-order reliability method is extended, through the incorporation of a linearization approach, to analyze the series system reliability of geotechnical problems. Two types of quasi-Newton approximations are applied to identify the design point and to compute the second-order probability of failure, respectively. The equivalent second-order reliability index is obtained using an approximation hyperplane for each limit state function. Then, the computed equivalent second order reliability indices can be employed, together with the corresponding unit direction vectors, to estimate the series system probability of failure using a linearization approach. Three geotechnical problems are employed to demonstrate the efficiency and accuracy of the suggested procedure, and advantages with respect to alternative computational tools are discussed. [ABSTRACT FROM AUTHOR]

The study proposes an improved Explicit Connectivity Bayesian Networks (ECBNs) for system reliability assessment. The framework combines Analytic Hierarchy Process (AHP) with the traditional ECBNs. AHP is adopted to consider probabilistic dependencies between system components. Judgment matrix is constructed and weight vector, which meets the requirement of a random consistency check, is extracted to describe a conditional probability information of BNs intermediate nodes. The framework is especially suitable for the system with rare damage filed data. In addition, considering the multiple failure modes for the system component, the multi-dimensional Performance Limit State (PLS) function is proposed to estimate the marginal probability for the BNs root nodes. PLSs are properly modeled as interdependent random variables instead of deterministic quantities. Failing to properly account for the dependencies between PLSs, the non-conservative failure probability results will be obtained. Finally, the system reliability can be calculated through the BNs Junction Tree forward inference algorithm, and the most vulnerable components in the system can be identified through backward diagnose inference. The improved ECBNs theory is first applied to the system reliability evaluation for a reinforced concrete bridge to verify its validity. [ABSTRACT FROM AUTHOR]

Conventional methods for analyzing system reliability involve the use of unit reliability and system composition. However, these methods have limitation on considering the type of failure distribution that exists in unit of system, resulting in inaccurate evaluation of system reliability as the bathtub curve. The process in which unit and system go from a normal working state to a failure state can be described as a fuzzy process. The fuzzy probability of unit reliability is defined in formula, and its curve is drawn based on bathtub curve, reliability function curve and fuzzy probability. For two systems that have the same system reliability, conventional methods also fail to take into account the effect of differences in unit reliability on system reliability. In this study, we introduce a novel method to analyze system reliability that based on fuzzy probability. This method is an assumption that unit reliability is a fuzzy probability-based event, and then these concepts of system failure probability and integrated system reliability are proposed. The system failure probability is a characteristic quantity used to measure the effect of different unit in system reliability. The integrated system reliability is a characteristic quantity that evaluated the comprehensive system reliability. These proposed concepts are applied in the reliability analysis of series system, parallel system and compound system. These results verify the effectiveness of the proposed method by the application and comparison in these examples. [ABSTRACT FROM AUTHOR]