Search

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Purpose: This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions. Design/methodology/approach: A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach. Findings: The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects. Originality/value: This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Aiming at the traditional FMECA (Failure Modes, Effect and Criticality Analysis) results of the threshing and cleaning system, which are strongly influenced by subjective factors, imprecise and easy to repeat, the fuzzy comprehensive evaluation method is introduced to quantify the results of the expert evaluation and reduce the subjective influence. Analytic Hierarchy Process (AHP) is used to assign weights to each influencing factor, and by calculating the comprehensive hazard level index, the hazard ranking of each failure mode is carried out, from which the critical failure modes are identified as the focus of improvement, so as to improve the reliability of the system. Comparison shows that the improved method effectively makes up for the shortcomings of the traditional FMECA analysis method, and it is easier to find out the critical failure modes, which provides a theoretical basis for practical application. [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]

The last few decades have witnessed advancements in intelligent metaheuristic approaches and system reliability optimization. The huge progress in metaheuristic approaches can be viewed as the main motivator behind further refinement in the system reliability optimization process. Researchers have intensively studied system reliability optimization problems (SROPs) to obtain the optimal system design with several constraints in order to optimize the overall system reliability. This article proposes a modified wild horse optimizer (MWHO) for SROPs and investigates the reliability allocation of two complex SROPs, namely, complex bridge system (CBS) and life support system in space capsule (LSSSC), with the help of the same process. The effectiveness of this framework based on MWHO is demonstrated by comparing the results obtained with the results available in the literature. The proposed MWHO algorithm shows better efficiency, as it provides superior solutions to SROPs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As the proportions of variable renewable sources (VRSs) such as solar and wind energy increase rapidly in the power system, their uncertainties inevitably undermine power supply reliability and increase the amount of operating reserve resources required to manage the system. However, because operating reserves have the characteristics of a public good and their value is related to the social cost of blackouts, it is difficult to determine their market price efficiently, which leads to inefficiencies in procuring operating reserves. This study estimates the operating reserve demand curve (ORDC) of the Korean power system to provide an effective basis for measuring the proper value and quantity of operating reserves needed to meet the reliability standard. A stochastic dynamic optimization model is applied to incorporate the probabilistic characteristics of VRS and the inter-hour constraint, which is necessary for analyzing load-following reserves. An econometric model and the Monte Carlo simulation method are used to generate the forecast profiles of solar and wind generation. The results indicate that the proper amount of hourly operating reserves needed in 2034 is approximately 4.4 times higher than that in 2020 at the current reserve offer price. The ORDC of 2020 has a price-inelastic shape, whereas the ORDC of 2034 has a price-elastic shape because the reserve requirement varies considerably with its offer price level in the high-VRS penetration case. This variability is due to alternatives, such as VRS curtailment or load shedding, which can replace the reserve requirement. This study also showed that VRS curtailment is an effective balancing resource as an alternative to reserves. [ABSTRACT FROM AUTHOR]

This paper studies the effect of the dependence state between basic events (BEs) on fault tree analysis (FTA) when the probabilities of events are characterized by interval values. The well‐known Frèchent bounds are extended for modeling six different types of dependence states between BEs. Three indices, called average dependence effect (ADE), location effect (LE) and size effect (SE), are defined for evaluating the effect of the dependence states between BEs on the probability of top event (TE) and identifying influential and non‐influential dependence states. Then, the proposed method is applied to fault tree (FT) examples, thereby explaining the dependence problem in FTA. To further verify the practicability of the method, FTA of the unilateral asymmetric movement failure of an aircraft flap mechanism is performed. The results show that: (i) the opposite and negative dependence contribute to the reliability of a parallel system while the perfect and positive dependence reduce it, (ii) the perfect and positive dependence contribute to the reliability of a series system while the opposite and negative dependence reduce it, and (iii) parallel systems are more reliable than series systems regardless of the dependence between BEs. [ABSTRACT FROM AUTHOR]

Inspired by the process of self‐healing of biological damage, high technology materials with self‐healing and self‐repairing mechanisms have been developed for high reliability and long lifetime. Therefore, the reliability modeling on intelligent systems with healing performance has become a research hotspot. Based on the diversity of healing mechanisms, this paper proposes a two‐phase reliability model method on self‐healing and self‐repairing systems. Impacts of environments, shock loads, self‐healing, and self‐repairing mechanisms are taken into account in this novel model. Besides, system lifetime and some reliability indexes under two shock models are derived, respectively. Moreover, Monte Carlo simulations are conducted to verify the accuracy of reliability under two models. Finally, an engineering case of metallized film capacitor is provided to illustrate the effectiveness and applicability of the proposed models by comparing numerical results and simulation results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

If the cooling system equipped with prime motor is not reasonably structured or lubricated sufficiently, the friction heat generated by decelerator during operation could not be dissipated effectively. To solve the problem with prime motor that its performance and efficiency both decline greatly due to the too high temperature of heat balance, it is necessary to study the temperature field and heat balance of the decelerator system, and this work is meaningful for improving the design of target system and ensuring its operation stability and reliability. To find answers of the said matter, this paper studied the heat balance features of decelerator system with its real-time reliability taken into consideration. At first, this paper analyzed the possible thermal failure modes, influence and fatality of the decelerator system, and elaborated on the method and steps of its real-time reliability analysis. Then, the decelerator system was modeled, its steady state temperature field was simulated in ANSYS Workbench, and the boundary conditions were built. At last, analysis results of the heat balance features of target system were given, which had verified the effectiveness of the proposed research method. [ABSTRACT FROM AUTHOR]

This paper focuses on hybrid renewable energy systems (HRES) as an alternative for conventional energy systems, especially fossil fuels. The problem is to find the optimal number of components of a standalone hybrid system, based on minimizing the value of the annualized cost of the system (ACS), in the presence of the constraints. To reduce the CO2 particles as one of the most threatening particles for the planet, an additional constraint is added to the objective function. For solving the problem of system sizing, Harmony Search and Ring Theory-based Evolutionary Algorithm have drawn attention to be preferred to numerous techniques. In addition,a combination of both, which has never been used before, is proposed. The proposed hybrid algorithm shows its ability to mitigate both the emissions and costs of the system promisingly in comparison with the others. Clearly, with Carbond = 15 and different values of LPSPMax as well as using RTHS, RTEA and HS algorithms for PV/WT/BSS/DG configuration, the best performance in terms of ACS minimization belongs to RTHS algorithm with the minimum cost of $1528.2. Also, considering LPSP = 0 and changing carbon from 0 to 20 tons/year, the ACS of the PV/WT/BSS/DG configuration is reduced from $38586.3 to $21153.5 (45% reduction). • Using a hybrid algorithm called RTHS to minimize the annual costs of the system. • Considering the LPSP constraint to supply the load demand at an acceptant level. • Reducing CO 2 emissions to increase the renewables penetration. • Considering uncertainty on both the generation and demand sides. • Validating of the proposed method based on real data for a case study in Iran.. [ABSTRACT FROM AUTHOR]

Existing research has been concentrated on improving the reliability of a distributed computing system through optimizing tasks allocation, providing software redundancy and providing hardware redundancy. None of these works considered the performance sharing mechanism in a distributed computing system. Different from other performance sharing systems whose reliability can be calculated directly, the reliability evaluation of a distributed computing system with performance sharing is more challenging since the reliability depends on the task execution time of each processor after performance sharing. This research considers a distributed computing system with performance sharing mechanism such that the computing power can be redistributed among different processors in the system. A reliability model is proposed to evaluate the distributed computing system with performance sharing. An optimization model is formulated to derive the optimal performance sharing policy such that the system reliability can be maximized. Both analytic examples and numerical examples are carried out to illustrate the proposed model and algorithm. [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, we discuss about the hierarchical Bayesian (HB) estimation concerning system reliability for a waste‐to‐energy (WTE) process. The main goal of this approach is to obtain this estimation WTE process reliability under different loss functions related series, parallel, and k‐out‐of‐m systems. In this case, we can drive system outcome by estimating each individual component. It is assumed that components are independent and identically distributed exponential random variables. Properties of the HB estimations under different loss functions are also provided, and comparisons are made between Bayesian and HB estimators via Monte Carlo simulation. The implementation of the proposed procedure was illustrated in detail by employing numerical practical examples extracted from a WTE process at the final part of this paper. [ABSTRACT FROM AUTHOR]

Increasing water demand due to socio-economic development often requires structure-level interventions, e.g., infrastructure expansion of water supply systems to bridge the gap between demand and water supply. Evaluating different expansion options is critical to making informed decisions. This study focuses on evaluating infrastructure investment options from the reliability perspectives of a water supply system. The evaluation framework is comprised of probabilistic demand projections, stochastic streamflow generation, a mixed-integer programming, and system performance evaluation. Different "industry understood" performance metrics, e.g., annual reliability, maximum delivery capacity, the probability of unsuccessful status, and the magnitude of potential water shortage, can be employed to evaluate the performance of water supply systems. This framework has been applied to Tampa Bay Water, a regional supply agency on the west coast of Florida, United States. The probabilistic demand projections for the future (2021–2040) consider socio-economic projections and climate conditions based on the statistics of historical observations. The Latin Hypercube Sampling algorithm is used to capture the bivariate distribution of supply and demand. This mixed-integer programming is a daily system optimization that minimizes operational cost and considers operational constraints and preferences, e.g., water withdrawal permits, facility production capacity, and water production preferences. Current infrastructure capacity and operational conditions are defined as the baseline scenario. Two additional planning scenarios of expanding the treatment capacity of a surface water treatment plant by 75,708 m3/day [20 million gallons per day (mgd)] and 113,562 m3/day (30 mgd) in the year 2028 are investigated. Results reveal that annual reliability would increase with the expansion of surface water treatment plant capacity. The spatio-temporal supply and demand variability determines that the firm yield from the surface water treatment is less than its expanded production capacity. Except for low demand realizations in the future, the duration and magnitude of potential water shortage could be reduced in both scenarios with engineering intervention. Although this framework is demonstrated for a regional water utility in Florida, it can be applied to other water supply systems around the world. [ABSTRACT FROM AUTHOR]

In the light of Industry 4.0 revolution, the Industrial Internet of Things (IIoT) allows digitalizing manufacturing processes and increasing the digital connectivity and automation in the smart factories and industrial systems. The reliability of a system is considered as a key performance indicator that defines how accurately and perfectly the system works. Ensuring reliability in the IIoT systems presents several challenges due to the presence of uncertain and dynamics in the industry environment as well as the complex nature of industrial systems. While there are no standard frameworks proposed and applied for the IIoT systems with multiple reliability levels, the challenges reveal many promising opportunities for the research and practical implementation including advancing technologies and systematic designs such as algorithms, architectures, and devices. However, there are a few works studying the reliability-related aspects of the industrial systems in the existing literature, they lack a framework to conduct the study and examination of reliability of IIoT systems in a holistic manner. In this regard, this article will elucidate and survey several performance-improvement methods and techniques for better reliability and further challenges to be overcome for highly reliable IIoT systems. Finally, by defining several open research issues, we expect to draw more attention for both the academic researchers and practitioners toward designing and developing reliable solutions and frameworks. [ABSTRACT FROM AUTHOR]

Degradation characteristics of lithium-ion battery pack system (LIBPs) cannot be well described directly by the existing life model of cell, such as the interference imposed by stochastic uncertainty and coupling effect of multiple cells. In this article, we devise a battery capacity estimation and prediction algorithm leveraging deep learning and least squares regression, and develop a stochastic model of capacity degradation obeying a lognormal distribution and a reliability evaluation methodology to reveal the internal evolutionary rules and health status of LIBPs with multi-state logic. We generate a comprehensive dataset consisting of 150 cells and 3 battery packs derived from the cloud platform for cross-validation and take real-world vehicle daily operating charging and discharging data as an input for the proposed framework. The validation results indicate that our best model achieves 0.33 % mean absolute percentage error and 0.44 % root mean squared percent error for state of health estimation and 0.317 mean absolute error in remaining lifetime prediction. Further, remaining capacity probability distribution curves are established with estimated and predicted results, and reliability fade tendencies are meticulously profiled based on the series-parallel structure, which enables accurate electric vehicle service time evaluation under different conditions of uncertainty factors introduced by battery inconsistency and randomness of driving behaviors. This work highlights the promise of deep learning to supplant resource- and time-consuming traditional approaches, and emphasizes the potential of employing reliability theory to rapidly design new-generation battery safety management frameworks. • A multi-channel separated hybrid neural network model aiming for achieving better accuracy and improved generalization. • Using only segments of the daily charging and discharging data with 30s sampling interval are capable of modelling and predicting the evolution of nonlinear battery systems. • Based on SOH estimation and RUL prediction at the cell level and reliability analysis at the pack level, the service time and safety status evaluation throughout the life cycle of electric vehicles is realized. • Premeditating the goal of building an excellent battery system, the impact of the series-parallel structure on the reliability of battery packs is investigated. [ABSTRACT FROM AUTHOR]

This paper presents a system reliability assessment of the structure of ballast-less railway tracks (SBLRTs) in which the major structural elements are considered. First, limit state functions (LSFs) with respect to serviceability and safety, including the maximum vertical deflection of rail, margin of safety of bending moment strength of upper and lower concrete beams, tensile yielding of steel bars reinforcing the upper concrete beam, along with the maximum pressure on subgrade are defined. Then, an adaptive soft Monte Carlo simulation (ASMCS) is established to model uncertainties associated with dynamic loads, characteristics of viscoelastic models, thicknesses of upper and lower concrete beams, and the train speed. Results of reliability analysis confirm the accuracy and computational efficiency of the ASMCS to take into account the uncertainties in the components of the SBLRTs. Sensitivity analysis indicates that the dynamic amplification factor as well as the stiffness of connection layer and subgrade course are the most influential random parameters. Meanwhile, providing a more uniform structure for these layers leads to the significant increase of reliability indices of all failure scenarios. Overall, the system reliability analysis of the components indicates that the tensile yielding of steel bars serves a crucial function in ensuring the safety of SBLRTs. [ABSTRACT FROM AUTHOR]

• Reliability estimation through simulation for multistate flow networks. • MP-based simulation without prior knowledge of boundary for demand. • Investigating reliability degradation through the analysis of time series. • Large-scale case study of Taiwan Academic Network. A multistate flow network is useful for the effective and efficient model construction and reliability evaluation of large and complex systems. To evaluate the reliability of a multistate flow network, boundary points for specified demands are generated in the existing path- and cut-based methodologies. Using the boundary points, the system reliability can be calculated using analytical or simulation methodologies. However, evaluating reliability using boundary points is an NP-hard problem. To improve the time efficiency of reliability estimation, a path-based simulation approach without boundary points is proposed in this study, where the minimal path without any cycles is used. The contributions of this study are threefold: First, the proposed path-based simulation complements the cut-based simulation to improve the applicability of reliability estimation for various network topologies. Second, the proposed simulation algorithm presents linear time complexity, whereas conventional boundary-based analytical methodologies or simulations consume exponential/factorial time. Third, the time attribute is included in the proposed simulation approach to analyze the behavior of reliability degradation over time. The experimental results, including those of a case study, indicate that the proposed path-based simulation is more effective and efficient than existing boundary-based approaches, particularly for large and complex systems. [ABSTRACT FROM AUTHOR]

It is still a challenging task to implement efficient methods for reliability analysis, especially for complex engineering systems that have implicit limit state surfaces and multiple failure modes. In this paper, an adaptive Kriging-assisted system reliability method is proposed to solve system reliability assessment problems. The proposed method uses an adaptive Kriging-based trust region to obtain the design point of each component failure mode based on the first-order reliability method in the first stage. By adding barrier functions to the potential limit state functions (LSFs) around the candidate design point, a new limit state function is obtained and used for the next failure mode identification in the second stage. After identifying all the potential failure modes, the system reliability assessment can be initially approximated by direct integration or binomial bounds theory. The accuracy of approximating failure probability can be enhanced by importance sampling or active learning in the third stage. Two case studies with quantitative landslide risk assessment are used to show the efficiency and accuracy of the proposed method. With the help of the smoothed-particle hydrodynamics method (SPH), the large deformation behaviours which may induce a strong nonlinear limit state surface can be used as the indicators of the failure consequences. The results show that the method proposed in this paper is capable of solving high non-linearity implicit LSFs. It also exhibits potential in handling problems with small failure probabilities. [ABSTRACT FROM AUTHOR]

• Integrated reliability data from subsystems and components to create an optimized RQT plan. • Combined Monte Carlo Simulation with RQT to ensure precise and comprehensive planning. • Objective approach to determining posterior risk despite limited knowledge. • Innovative methods optimized RQT plan, reducing test time while fulfilling risk requirements. During the development of systems, traditional System Reliability Qualification Testing (SRQT) is typically utilized to assess whether they meet predefined reliability standards. However, this approach often demands substantial sample sizes and prolonged test durations, rendering it impractical for costly, highly reliable systems with limited sample sizes. Additionally, the extended test duration may not align with practical time-to-market pressures or budget constraints. To overcome these challenges, the study integrates subsystem data into the monopropellant engine system RQT plan's design. By leveraging Monte-Carlo simulation, subsystem data is modeled to create a system parameter distribution, enabling the formulation of SRQT plans based on posterior risks. An example of a monopropellant liquid rocket engine system is provided to demonstrate the advantages and applications of the proposed methodology. [ABSTRACT FROM AUTHOR]