Reliability Analysis Of Systems Research Articles

System Reliability Analysis of Site Slope Using the Conditional Spatial Variability of Soil Properties

System Reliability Analysis of Site Slope Using the Conditional Spatial Variability of Soil Properties

Active learning-based structural reliability evaluation Kriging model and sequential importance sampling

The efficient analysis of structural system reliability with multiple failure modes and small failure probabilities presents significant challenges for active Kriging methods. This paper proposes a novel method, PAK-SEIS, which combines the Parallel Active learning Kriging model and the Sequential Importance Sampling method to overcome these challenges. The proposed method includes a new sequential importance sampling method that integrates the sequential Monte Carlo simulation (MCS) and kernel density estimation to construct the optimal importance density. Additionally, the proposed parallel learning strategy allows for the selection of multiple new training samples, reducing performance function calls and refining iterations of Kriging models. The PAK-SEIS method gradually approximates the optimal importance density by updating multiple new training samples and failure modes in parallel at each iteration based on a small number of candidate samples. Candidate samples generated in the final sequence can cover all limit state surfaces, and all Kriging models are accurately constructed. The PAK-SEIS method can effectively address system reliability problems with small failure probability, multiple failure regions, and implicit functions. Two examples demonstrate the efficiency and accuracy of the proposed PAK-SEIS method.

Reliability analysis and optimal redundancy for a satellite power supply system based on a new dynamic k-out-of-n: G model

Reliability analysis and optimal redundancy for a satellite power supply system based on a new dynamic k-out-of-n: G model

Time-dependent system reliability analysis using adaptive single-loop Kriging with probability of rejecting classification

Time-dependent system reliability analysis using adaptive single-loop Kriging with probability of rejecting classification

System reliability analysis of mooring system for floating offshore wind turbine based on environmental contour approach

The system reliability of the mooring system for floating offshore wind turbines (FOWT) is analyzed in this paper. Various failure modes such as mooring line breaking, fatigue and floater offset are included and solved as a series system. This work employed a new mooring breaking assessment algorithm, wherein the effect of fatigue damage on breaking strength is simulated by introducing functional dependences into the coupled dynamic analysis. In this research, the extreme design loads are estimated using the environmental contour (EC) approach and a logarithmic function is proposed to describe the relationship between the extreme response and the target return period to simplify the full long-term analysis. The time-domain coupling analysis of the FOWT is performed considering all one-year sea states, the long-term fatigue damage was approximated based on linear criterion. The fatigue damage can contribute significantly to the breaking strength of mooring lines. The results indicate that ignoring the dependence between the residual strength and cumulative fatigue damage would cause under-estimated results. The calculated failure probability of the floating system is larger than that of any single failure mode, while it is mainly dominated by the main single failure mode with the largest failure probability.

Profit Analysis to an Industrial System Possessing Active Redundancy form Using Geometric Distribution

This paper elaborates on the approach towards reliability analysis for the active redundant system with a dual nature of repair strategy according to the kind of failure. The reliability characteristics using regenerative techniques are analyzed stochastically for the system, which includes two parallel units acting in an active redundancy form. A transition model has been designed for the working mechanism discussed in the paper and evaluated with the help of a geometric distribution possessing distinct repair. Regenerative techniques and geometric distribution were used to derive the numerical equations for reliability parameters such as availability, mean time to system failure, and repair mechanisms following down period of the system. A graphical analysis of the repair/failure rate with respect to the profit function has also been presented.

An adaptive method based on PC-Kriging for system reliability analysis of truss structures

In practice, a truss consists of a large number of members which makes it a complex system. This leads to difficulties to estimate the system reliability due to computational costs. An adaptive method is thereby proposed to deal with this issue. It constructs a global metamodel to quickly estimate the rough reliability index of a truss. According to the estimated reliability index, the differential evolution algorithm is performed to generate more samples located in an expanded domain so that more representative failure modes can be identified. Combined with AK-SYSi, local metamodels of representative failure mods are built, and updated through active learning. When the convergence criterion is satisfied, the results of system reliability analysis can be obtained. Eventually, two examples of truss structures are studied to illustrate the superiority of the proposed method in balancing accuracy and efficiency. The results indicate that the proposed method makes a good balance between accuracy and efficiency when it is applied to analyze the system reliability of the truss.

System Reliability Analysis of Large Capacity Electric Mining Trucks Used in Coal Mining

In today’s world where competition is increasing, the focus of all efforts on meeting production targets at the lowest possible cost has increased the need for maximum benefit from existing machinery/equipment. In this context, availability, reliability and maintainability analysis have become the basic tools to be used in meeting this need. In this study, reliability, maintainability and availability values were determined using the operation and repair data (2019) of large capacity electric trucks used in the Manisa-Soma coal field. First of all, each truck was analyzed and then the analysis of the entire truck system was performed. In this context, RAM analysis of all trucks was performed and then the general reliability of the entire truck system was calculated by considering the working order in the enterprise. According to this; The truck with the highest reliability is the truck with door number 555 and the reliability rate is 77.04%, and the truck with the lowest reliability is the truck number 556 with a rate of 26.31%. As a result of the maintainability analysis, the truck with the 553 door number had the highest maintainability with a rate of 73.52%. The truck numbered 549 has the lowest maintainability rate with a rate of 42.21%. As a result of the system analysis, the reliability rate was determined as 24% in R (1000) minutes, 59% in R (500) minutes and 83% in R (250) minutes.

New prediction method for reliability analysis of ocean sustainable energy systems

With the aim of overcoming the shortcomings of traditional approaches, in this paper, an exponential Weibull distribution for fitting the significant wave height data is proposed in order to calculate more accurately the sea-state parameter distribution tails and to extrapolate well. This proposal is applied in predicting the probability distribution tails of a measured ocean wave dataset, and its accuracy is clearly demonstrated. The proposal is subsequently utilised in combination with Monte Carlo sampling to form a new environmental contour method for deriving a 50 year contour line based on the aforementioned measured ocean wave dataset. The derived environmental contour line is compared with that predicted by using a traditional contour line approach. Additionally, the engineering significance of using a more reliable environmental contour line, such as the one derived using the proposed new method for predicting the long-term design force values for an ocean sustainable energy system is highlighted. In conclusion, the newly proposed environmental contour method is recommended to be used when predicting the extreme dynamic response values for the safe and successful design of ocean sustainable energy systems.

Reliability Analysis of Cold-standby Systems with Subsystems Using Conditional Binary Decision Diagrams

<p>Cold-standby systems have been widely used for conditions with limited power, which achieve fault tolerance and high-reliability systems. The cold spare (CSP) gate is a common dynamic gate in the dynamic fault tree (DFT). DFT with CSP gates is typically used to model a cold-standby system for reliability analysis. In general, inputs of the CSP gate are considered to be basic events. However, with the requirement of the current system design, the inputs of the CSP gate may be either basic events or top events of subtrees. Hence, the sequence-dependency among basic events in CSP gates becomes much more complex. However, the early conditional binary decision diagram (CBDD) used for the reliability analysis of spare gates does not consider it well. To address this problem, the conditioning event rep is improved to describe the replacement behavior in CSP gates with subtrees inputs, and the related formulae are derived. Further, a combinatorial method based on the CBDD is demonstrated to evaluate the reliability of cold-standby systems modeled by CSP gates with subtrees inputs. The case study is presented to show the advantage of using our method.</p> <p> </p>

Parametric Evaluation Techniques for Reliability of Internet of Things (IoT)

Reliability analysis aims to assess the dependability and performance of a system over time, considering factors such as device failures, communication issues, and data integrity. By integrating taxonomy and reliability analysis, researchers can systematically analyze and enhance the reliability of IoT systems by addressing specific components and categories identified in the taxonomy. IoT based on its reliable applications is classified among various categories such as smart home devices, smart watches, electric vehicles with IoT capabilities, industrial, healthcare, agriculture, soil monitoring sensors, smart city infrastructure, environmental monitoring devices, and smart energy management devices. The need for a consistent taxonomy was one of the problems recognized and examined in this paper even though IoT management technology is developing. To examine the reliability analysis of IoT systems, we created a brand-new taxonomy for IoT gadgets that places a strong focus on administration. We have released a completely new IoT reliability RBD model analysis at different gateways and Round-Trip Time is to be analyzed. It features an aircraft for dependability. Our goal is to help administrators of networks, as well as designers, put processes in place that will analyze and enhance the reliability of IoT systems. To implement techniques to increase the IoT system's reliability, we may utilize the management capabilities to keep an eye on the devices. ChatGPT can act as the conversational interface for ISABELA. By integrating ChatGPT with ISABELA's functionality, users can have natural language conversations with ISABELA, making the interaction more intuitive and user-friendly. A use case study of ISABELA has enhanced the position of the CHATGPT system in the IoT system which increase the efficiency and reliability of IoT designs and networks.

Hamiltonian MCMC methods for estimating rare events probabilities in high-dimensional problems

Accurate and efficient estimation of rare events probabilities is of significant importance, since often the occurrences of such events have widespread impacts. The focus in this work is on precisely quantifying these probabilities, often encountered in reliability analysis of complex engineering systems, based on an introduced framework termed Approximate Sampling Target with Post-processing Adjustment (ASTPA), which herein is integrated with and supported by gradient-based Hamiltonian Markov Chain Monte Carlo (HMCMC) methods. The developed techniques in this paper are applicable from low- to high-dimensional stochastic spaces, and the basic idea is to construct a relevant target distribution by weighting the original random variable space through a one-dimensional output likelihood model, using the limit-state function. To sample from this target distribution, we exploit HMCMC algorithms, a family of MCMC methods that adopts physical system dynamics, rather than solely using a proposal probability distribution, to generate distant sequential samples, and we develop a new Quasi-Newton mass preconditioned HMCMC scheme (QNp-HMCMC), which is particularly efficient and suitable for high-dimensional spaces. To eventually compute the rare event probability, an original post-sampling step is devised using an inverse importance sampling procedure based on the already obtained samples. The statistical properties of the estimator are analyzed as well, and the performance of the proposed methodology is examined in detail and compared against Subset Simulation in a series of challenging low- and high-dimensional problems.

Competing Failure Modeling for Systems under Classified Random Shocks and Degradation

A new model is presented to analyze reliability for systems subjected to competing risks of a typical degradation process and external random shocks. The external random shocks may cause hard failure and instantaneous increases in the degradation process. Random shocks are classified into three types in the new model according to their sizes. Different types will lead to different instantaneous increases in the degradation process. Besides system reliability, the competing failure probabilities are also studied. Competing failure probability indicates the probability that one risk mode fails while the other risk mode does not fail. Competing failure probabilities are helpful in determining which risk mode is more likely to occur during a system’s lifetime. It is impractical if both risk modes fail together for systems under competing failure modes, while the system reliability and failure probabilities, which are simply calculated by expression, do not resolve this impractical situation. The modified values of system reliability and competing failure probability are calculated in this paper with the impractical situation excluded. The effectiveness of the presented model is demonstrated by the reliability analysis of the micro-electromechanical system (MEMS), which proved that the probabilities calculated using this method are more practical. In addition, sensitivity analysis is performed for specific parameters.

Reliability analysis of repairable multistate phased mission systems with Markov approach based on states

PurposeThe purpose of this study is to find the reliability of the three-component three-phased mission system, which can be repaired by considering the exponential distribution for repair and failure rates in the transitions between the phases based on states with Markov approach. Also, multilevel-phased mission systems are calculated based on states for partially working states.Design/methodology/approachThe reliabilities of the repairable two-level and three-level three-component three-phased mission systems based on states are calculated with the Markov approach. The structure functions are obtained for each phase of the systems, and differential equations are created by the failure and repair of each working state component. These equations are solved using Laplace method.FindingsReliability values of two-level and three-level three-component three-phased systems with different failure, repair, and time intervals are calculated and compared. The intermediate states that multilevel systems handle differently from two-level systems provide a better investigation of the systems. So, these repairable systems offer transparent information in complex systems like transportation and energy, ensuring appropriate timing and cost for repair operations.Originality/valueThis study is original in terms of calculating the reliability of the repairable phased mission system based on the states using Markov method. It is also important in calculating the reliability of the repairable multilevel phased mission system based on states and making reliability comparisons according to different repair and failure rates, equal and different time intervals.

BIM–based time-varying system reliability analysis for buildings and infrastructures

In recent decades, building information modelling (BIM) technology has been rapidly developed and applied widely in the building industry. Although BIM technology is crucial for the whole design, construction, operation, and maintenance life cycle of building structures, BIM-based infrastructure time effect analysis still has limitless potential. Nevertheless, the current BIM-based study on time effect focuses mostly on cost and schedule analyses for buildings. The assessment of infrastructure places greater emphasis on component level evaluation and ignores how components affect system safety. In this paper, a BIM-reliability integrated technology for time-varying and system analysis of structural safety is developed. In the developed framework, parameters information for calculating component reliability are first extracted from the BIM model. The BIM model is then transformed into a topology structure and based on the transformed structure, the risk assessment is conducted through stochastic approaches. The suggested framework is demonstrated using two different cases: a long-term corrosion impact analysis example of an underground pipeline network and a short-term seismic load analysis instance of a four-layer frame. In the short-term analysis, the structural analysis is performed by integrating REVIT-Dynamo and OpenSees, taking into account the randomness of the material properties and the fabrication size. The design optimization can be carried out by comparing the reliability of inter-storey displacement under seismic load in different design schemes, and the system failure probability is more sensitive to the changes in the failure probability of each floor. In the long-term analysis, the time-varying reliability and importance index of each pipe during its lifetime are calculated using the transformed topology algorithm and the stochastic corrosion model. It is found that the BIM-reliability integrated technology can effectively evaluate the impact of time effect on the safety of the structures and is of great significance for structures with unknown working environment and working conditions.

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.

An improved active Kriging method for reliability analysis combining expected improvement and U learning functions

The reliability assessment of structures with multiple failure modes and small failure probability is challenging due to the time-consuming simulations required. Active learning Kriging methods for structural reliability with multiple failure modes have shown high computational efficiency and accuracy. However, selecting the appropriate sample and its failure mode to update the Kriging models remains a key problem. In this paper, we propose a new learning function and stopping criterion to further improve the efficiency of structural system reliability analysis. Firstly, we propose a new learning function that combines the expected improvement function and the U learning function. This function selects the most suitable samples, balancing the degree of expected improvement of samples to the limit state surface and the degree of misclassification probability of samples. Secondly, we propose a new stopping criterion that considers both the accurate construction of limit state surfaces and the probability of accurately predicting the signs of samples. This criterion avoids premature or late termination of the active learning process. Thirdly, the sequential MCS simulation method is employed in the active learning process to efficiently evaluate small failure probability problems. By analyzing four examples, we verify the accuracy and efficiency of the proposed structural reliability analysis method.

Reliability analysis for system with dependent components based on survival signature and copula theory

Recently, survival signature theory has been widely used in system reliability analysis. However, there are few studies that address the extension of survival signature theory to component-dependent systems. This study proposes a copula-based survival signature method to obtain an analytical solution for system reliability considering component dependence. It consists of two steps: constructing the dependence structure and utilizing the copula function to compute the analytical probability structure. The effectiveness of the Archimedean copula function in describing component dependence and its compliance with the exchangeability assumption in survival signature theory are first derived. Then, using Sklar's theorem, the analytical solution for the probability structure is determined based on the inclusion-exclusion principle. Finally, the hierarchical Archimedean copula function is employed to build a more sophisticated dependent structure. It is demonstrated that the satisfaction of the exchangeability assumption under the hierarchical Archimedean copula structures depends only on whether the joint distribution probability function of the components in the same type is constructed by a unique Archimedean copula function. Three separate systems with imprecise dependent information are employed in the reliability analysis in order to show the validity of the proposed method. The findings show that given a straightforward series-parallel system with a single type of component, the system reliability initially drops and then rises as the dependence parameters in the copula function grow at each time point. Additionally, the approach is more precise and effective than the simulation method when it comes to the study of complex systems.

Reliability Analysis of Power System Considering Flexibility Resources

Flexible resources are a critical resource in ensuring the security and steady running of a large percentage of electricity systems that use new sources of energy. The power system’s flexible resources participate in power regulation, improving the power system capability to absorb new energy and also the electricity system’s reliability. This paper calculates the dependability index of electricity system using the basic calculation method of the equivalent energy function. By flexibly handling the new power generation curve’s adjustable capacity and flexible resources, new energy generation and flexible resources count toward equivalent energy calculation, and the dependability index of the electricity system is calculated. The efficacy of this approach is illustrated by an example.

Dynamic IGBT Three-Dimensional Thermal Network Model Considering Base Solder Degradation and Thermal Coupling Between IGBT Chips

With the development of power electronic technology, the thermal characteristics of high-power insulated gate bipolar transistor (IGBT) module are vital information for thermal management of power electronic equipment, reliability analysis, and thermal design of power electronic system. However, the present commonly used lumped thermal network model based on thermal resistance and heat capacity still has some limitations in accurately predicting the junction temperature of IGBT modules, significantly once considering the degradation of base solder in high-power IGBT modules. In this paper, the thermal behavior of high-power IGBT chips under different base solder degradation is studied by the finite element method (FEM). The results show that the degradation of the base solder has effects on the thermal impedance between the junction and the case, which should be fastidiously thought about within the thermal network model modeling. As a result, a dynamic three-dimensional thermal network model that takes into account the thermal interaction between IGBT chips, as well as the degradation of the base solder, is proposed in this paper. The proposed thermal network model can estimate the junction temperature of a high-power IGBT module correctly and fast based on the real degradation of the base solder. Finally, finite element simulation and experimental findings are used to validate the proposed dynamic three-dimensional thermal network model. The results show that the dynamic three-dimensional thermal network model has comparable accuracy to finite element simulation and test results. At the same time, the dynamic three-dimensional thermal network model responds faster than the finite element simulation.