Reliability Analysis Of Systems Research Articles

Linear Consecutive-k-out-of-n: G System Reliability Analysis

The concerned study pertains to the development of a new stochastic model for the reliability analysis of linear consecutive-k-out-of-n: G system, where k>n2. In the developed model, system may collapse as a result of common cause failure or hardware failure in its units. The system has exponentially distributed failure rates, and in case of breakdown, it is repaired with the copula method. The developed model has been examined through supplementary variable technique (SVT) along with Laplace transform. The current paper has specifically studied consecutive-(n-1)-out-of-n: G system. The performance of such system having ten components is explored and its various reliability measures have been obtained and discussed with the help of graphs. The originality of this work lies in incorporating common cause failure in conjunction with copula repair in the reliability modeling of consecutive systems through the SVT. The study confirms that an increase in failure rates and the number of components of the concerned system decreases mean time to failure (MTTF). The profit of linear consecutive-9-out-of-10: G system is examined with the help of a numerical example.

Efficient model-correction-based reliability analysis of uncertain dynamical systems

The scope of this paper is to apply a model-correction-based strategy for efficient reliability analysis of uncertain dynamical systems based on a low-fidelity (LF) model whose outcomes are corrected in a probabilistic sense to represent the more realistic outcomes of a high-fidelity (HF) model. In the model-correction approach utilized, the LF model is calibrated to the HF model close to the so-called most probable point in standard normal space, which allows a more realistic assessment of the considered complex dynamical system. Since only few expensive limit state function evaluations of the HF model are required, an efficient reliability analysis is enabled. In an application example, the LF model describes an existing single-span railway bridge modelled as simply supported Euler–Bernoulli beam subjected to moving single forces representing the axle loads of a moving train. The HF modelling approach accounts for the bridge–train interaction by modelling the passing train as mass-spring-damper system, however increasing the computational effort of the limit state function evaluations. Failure probabilities evaluated with the model-correction approach are contrasted and discussed with failure probabilities of the sophisticated bridge–train interaction model evaluated with the first-order reliability method (FORM). It is demonstrated that the efficiency of the method depends on the correlation between the LF and the HF model. A comparison of the results of FORM and the model-correction-based approach shows that the latter provides reliable failure probability prediction of the HF model while leading to a significant reduction in computational effort.

Reliability analysis and optimization for a redundant system with dependent failures and variable repair rates

This paper considers the reliability analysis and the bi-objective optimal problem for a redundant system with two dependent failures: common cause failure (CCF) and load-sharing failure, where the system contains N active components, W warm standbys and C cold standbys, a K-mixed redundancy strategy is considered. In the system, there is a repair team providing variable repair rates according to differential situations: when there are standby components available in the system, the repair team assigns a regular repairman to repair the failed components, with the known fact that regular repairman is not as skilled as an expert repairman in doing some complex repair; when all the spares are exhausted, the repair team assigns an expert repairman to attend failed components; when the system breaks down due to a CCF’s occurrence, the repair team provides a very special repair service to restore the system to normal working state. The steady-state distribution of the system is obtained by matrix-analytic method. The sets of equations satisfied by the reliability functions and transient availabilities from arbitrary initial state are respectively presented and solved by Markov renewal theory, Laplace–Stieltjes transform (LST) and Laplace transform (LT) technique. Based on the obtained results, sensitivity analysis is performed by some numerical experiments. From economic view point, the bi-objective optimization problem is constructed and solved by NSGA-II algorithm for the minimal expected total cost and maximal availability.

Uncertainty analysis of extreme mooring loads associated with environmental contours and peak tension distributions

This paper aims to assess the uncertainty on the extreme mooring loads of floating system considering short-term variability. Two environmental contour approaches based on the inverse First and Second Order Reliability Methods are employed to identify critical sea states that may give rise to extreme loads. The uncertainty related to the construction of environmental contours is addressed including significant differences due to marginal distribution fitting, parameter estimation methods and joint models. Three measured datasets are analysed using a known conditional joint distribution and proposed mixed copula model. 3-h time domain numerical simulation for each sea state is conducted and the characteristic extreme responses of mooring lines subjected to design loads are assessed. The uncertainties due to various statistical models including the average conditional exceedance rate method as well as global maxima, peak-over-threshold method combined with Gumbel distribution, Generalized Extreme Value distribution, Generalized Pareto distribution and 3-parameter Weibull distribution are investigated and quantified. It is observed that marginal distributions, joint models and parameters estimation methods have apparent effect on design loads estimation, and the extreme tensions of the semi-submersible platform shows significant difference using various probabilistic models. The results indicate that those epistemic uncertainties should be account for in the reliability analysis or safety factor calibration for mooring systems.

An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state

• A new MRF learning function for multimode structure system is proposed. • A contribution function is established and explicitly derived. • A reasonable convergence criterion is employed. • The accuracy and efficiency of the proposed method are verified by five examples. For efficiently analyzing reliability of multimode structure system by adaptive Kriging model combined with Monte Carlo simulation, a new most reduction learning function (MRF) is proposed in this paper. Firstly, a contribution function is established to quantify the effect of candidate sample point (CSP) on reducing average probability of misjudging single mode structure state in the differential domain adjacent to CSP. Secondly, the contribution function is explicitly derived after selecting a reasonable size of the differential domain. Thirdly, by the maximum expected contribution function of single mode structure and the logical relationship among modes in multimode structure system, the new learning function named MRF is obtained. The main novelty of this work is proposing a new MRF based strategy for multimode structure system reliability analysis, in which it considers the logical relationship among modes in multimode structure system, probability of misjudging single mode structure state, probability density function and the absolute value of Kriging prediction of single mode structure at CSP. Compared with the up-to-date methods, the proposed strategy for multimode structure system reliability analysis is easier and more efficient to be executed, and its superiority is verified by numerical and engineering examples.

Modelling of heating furnace fire scenarios using fault tree analysis, a bayesian network, and a thermal transfer method for system reliability analysis

Energy production for oil companies requires high-performance equipment. However, after a certain period of use, this equipment could be damaged, causing numerous problems and defects, impeding production, and leading to incidents that may cause material and human losses. Therefore, it is necessary to master the performance parameters of each equipment, which are key indicators of its functionality and can indicate the possibility of reusing it after inspection and diagnosis. However, it is worth noting that the studied system is a vertical cylindrical furnace in Algeria's refinery. This paper describes a new hybrid approach, a methodology that evaluates system reliability by combining two studies. The first is based on the surety of the system's functioning through an analysis with a fault tree to identify the risks, followed by a quantitative analysis using a Bayesian method. The second study follows the thermal calculation of the equipment's performance parameters, namely: the thermal efficiency (η), heat transfer coefficient (h), and heat absorbed quantity (Q), using heat balance equations to quantify the heat absorbed quantity and identify the fluid flow regime inside the tube. The calculation considers three cases: The design case, the failure case, and the after renovation case. The originality of this work appears when the collected findings from the second study are fed into the first study's Bayesian Network (BN) to analyse the system's reliability using the calculated parameters. Then, BN is used to calculate the Probabilistic Importance Factors (PIFs) in order to identify the most impactful system components. Finally, a prediction analysis was conducted. The latter reveals that one of the furnace's major faults is the fumes accumulation inside the combustion chamber. Corrosion, overheating, coke formation inside the tubes, dumper deformation, and burner tip clogging all contribute to this fault.

Analysis of Information System Implementation, Compensation and Reliability of Internal Control to the Financial Fraud Prevention

This research examines the implementation of information system, compensation, and internal control toward the effect on financial fraud prevention. The study conducted in one of the largest chemical distribution company in Indonesia, with the financial staff of the headquarter as the respondent. Data gathered by using total sampling, and examine by using regression analysis. Result shows that the implementation of information system and compensation can prevent financial fraud, but compensation is not. the significant of the result is, compensation works on preventing financial fraud when the information system and internal control applied. To develop stringer impact, future research expected to have wider scope of respondent and using smaller alpha score to see the different of the research and to add the qualitative perspective.

Discrete-Time Bayesian Networks Applied to Flexible Coping Strategies of Nuclear Power Plant Systems

The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risk in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by the Nuclear Energy Institute to manage the challenges of BDBEEs and enhance reactor safety. Due to the uniqueness of the FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. In this study, we investigate the effectiveness and applicability of both Bayesian networks (BNs) and discrete-time Bayesian networks in the reliability analysis of FLEX equipment. The study compares BNs with two other reliability assessment methods: fault tree and Markov chain. These methods are also shown to be capable of mapping into BNs to perform a reliability analysis of FLEX systems. A neutral dependency algorithm is used to simplify the conditional probability tables and reduce the complexity of the BNs. The results indicate that BNs are not only a powerful method for modeling FLEX strategies but are also effective techniques for inclusion of the dynamics of FLEX equipment in probabilistic risk analysis.

Liquid chromatography-quadrupole orbitrap and liquid chromatography-tandem mass spectrometry system for rapid identification and quantitation of thirty nonsteroidal anti-inflammatory drugs and acetaminophen in illegal products.

As the public interest in healthcare increases, illegal dietary supplements, foods, and drugs containing unauthorized pharmaceutical ingredients, including nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen, have been identified. Excessive and unintentional consumption is toxic to the gastrointestinal tract, kidneys, and liver; therefore, these pharmaceuticals must be monitored using analytical methods. A rapid and reliable analysis system involving liquid chromatography-quadrupole orbitrap mass spectrometry (LC-Q-Orbitrap/MS) and liquid chromatography-tandem mass spectrometry (LC/MS/MS) was established and validated to identify and quantify 30 NSAIDs and acetaminophen. In addition, we obtained the MS2 spectrum for each component with the proposed structure of the fragment ions. The analytical method was applied to 505 samples of illicitly distributed dietary supplements, foods, and pharmaceuticals. Non-steroidal analgesics were detected in 126 samples. Carbamazepine (42.9%) and diclofenac (30.2%) were the most detected components in the samples; other pharmaceutical adulterants were also detected in some cases. Additionally, we present the identification of an unknown component, dexamethasone (799 μg/g), using LC-Q-Orbitrap/MS in a sample containing the unknown component with meloxicam (15.4mg/g). The developed analysis system, consisting of qualitative analysis using LC-Q-Orbitrap/MS and quantitative analysis using LC/MS/MS, can rapidly and accurately identify and quantify NSAIDs and acetaminophen while also identifying non-analytical components.

A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis

The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called UA, which takes the correlation between training points and samples into account. The UA function has been developed from the U function but is distinct from it. The UA function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since UA and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.

System reliability analysis of landslides involving sliding surfaces using intelligent multiple response surfaces

The shear strength reduction method provides an effective tool of numerical analysis for landslides reliability analysis. However, it ignores the failure probability of the secondary failure surfaces and requires huge computational cost. To avoid these common criticisms, an intelligent multiple response surfaces method for system reliability using multiple response-surface method (MRSM) and least-squares support vector machine (LSSVM) is presented to evaluate the stability of complex multistage historic landslides with multiple sliding surfaces. Deterministic analysis of each sliding surface is first performed using the finite element method of sliding surface stress analysis, which is applied to obtain the safety factors of different sliding surfaces from the stress fields generated by finite element simulations. The LSSVM model with excellent nonlinear fitting ability is then employed to construct the multiple response-surface method (MRSM) of the sliding surfaces and a genetic algorithm (GA) is adopted to optimize the LSSVM. This proposed methodology is finally applied to investigate the probability of system failure of the Zhenggang landslide in southwestern China. The results indicate that the proposed approach can reduce the computational cost of finite element analysis in direct Monte Carlo simulation (MCS) by proper training using a limited of samples, and the calculation accuracy meets the engineering requirements of complex multistage historic landslides.

Error-guided method combining adaptive learning kriging model and parallel-tempering-based importance sampling for system reliability analysis

This study investigates the rare-event system reliability analysis with numerous failure regions. A novel method, based on the parallel tempering (PT) and importance sampling (IS) technique, is proposed. Surrogate models (kriging model) are built for the true performance function and a probabilistic classification function is derived to predict the failure regions. The PT algorithm is used to obtain points populating all of the predicted failure regions. Representative points are identified using the k-weighted-means clustering method. A Gaussian mixture model is formulated by the representative points and importance samples are simulated accordingly. The optimal training points are selected to update all surrogate models. In the framework of IS, the terminating criterion for assessing the estimation error of the system failure probability is devised. The learning process is terminated at the appropriate stage. The method is termed active learning kriging–parallel tempering–importance sampling (ALK-PT-IS). Four numerical examples illustrate the effectiveness and precision of this method.

Methodology for updating GO-FLOW model to handle scenario changes in nuclear power plants

The design of a nuclear power plant is proved to be safe enough in various hypothetical operation scenarios after strict safety assessment. One of the important tasks of operational risk management in a nuclear power plant is to evaluate whether any configuration change of the nuclear power plant can still achieve its expected safety and economic goals. This paper proposes a system reliability modeling and analysis method based on two-layers hierarchical GO-FLOW model. By flexibly adjusting the parameters of a GO-FLOW model, the model can adapt to the changes of success criteria and various configuration of the modeled system, thus avoiding the extra workload brought by re-modeling and improving the efficiency of risk management in nuclear power plants.

System Reliability Analysis for Truss Structures Based on Automatic Updated�Model

Multiple failure modes tend to be identified in the reliability analysis of a redundant truss structure. This identification process involves updating the model for identifying the next potential failure members. Herein we intend to update the finite element model automatically in the identification process of failure modes and further perform the system reliability analysis efficiently. This study presents a framework that is implemented through the joint simulation of MATLAB and APDL and consists of three parts: reliability index of a single member, identification of dominant failure modes, and system-level reliability analysis for system reliability analysis of truss structures. Firstly, RSM (response surface method) combines with a constrained optimization model to calculate the reliability indices of members. Then the β-unzipping method is adopted to identify the dominant failure modes, and the system function in MATLAB, as well as the EKILL command in APDL, is used to facilitate the automatic update of the finite element model and realize load-redistribution. Besides, the differential equivalence recursion algorithm is performed to approximate the reliability indices of failure modes efficiently and accurately. Eventually, the PNET (probabilistic network evaluation technique) is used to calculate the joint failure probability as well as the system reliability index. Two illustrative examples demonstrate the accuracy and efficiency of the proposed system reliability analysis framework through comparison with corresponding references.

Reliability Analysis of Stochastic System using Stochastic Spectral Embedding based Probability Density Evolution Method

This study presents a reliability analysis of stochastic system using the probability density evolution method (PDEM). The PDEM is formulated according to the principle of probability conservation where generalized density evolution equations (GDEEs) are completely decoupled. To estimate the probability density function accurately, a set of representative points of random variables are generated using the GF-discrepancy scheme. A large number of representative points is needed to obtain satisfactory accuracy, which becomes computationally expensive. To reduce the computation burden, a stochastic spectral embedding (SSE) is used as a surrogate model which approximates the original response surface. To illustrate the proposed SSE-based PDEM, two numerical examples are investigated, including the reliability analysis of four-branch problem, and the reliability-based design optimization of a shape memory alloy based damped outrigger tall timber building. Numerical results show that the proposed SSE-based PDEM can estimate failure probability using a very small number of representative points without compromising accuracy compared with Monte Carlo simulation, which leads to a reduction in computational costs.

Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm

The reliability of contact network is always an important part of the reliability analysis of traction power supply system. In this paper, combined with the failure rate data of the main parts of the contact network, the small sample data is expanded by bootstrap non-parametric regeneration sampling method using the fused particle swarm least squares support vector machine (PSOLSSVM) algorithm to provide training set data for particle swarm optimisation. Mann, Schuer and Fertig fit test and the two-parameter Weibull distribution of the model based on MATLAB were used. The key components such as load cable and insulator were selected, and the data was combined to establish a model to characterise the overall reliability of the contact network. Based on the established model, the relevant parameters of each main part are estimated separately, which accords with the actual situation.

Reliability and performance analysis of a series-parallel photovoltaic system with human operators using Gumbel-Hougaard family copula

Reliability and performance analysis of a series-parallel photovoltaic system with human operators using Gumbel-Hougaard family copula

Reliability Analysis of the Financial Management Auxiliary System of Remote Branches Based on Cloud Computing

Reliability Analysis of the Financial Management Auxiliary System of Remote Branches Based on Cloud Computing

Reliability analysis of s-out-of-k multicomponent stress-strength system with dependent strength elements based on copula function.

This paper considers the reliability analysis of a multicomponent stress-strength system which has $k$ statistically independent and identically distributed strength components, and each component is constructed by a pair of statistically dependent elements. These elements are exposed to a common random stress, and the dependence among lifetimes of elements is generated by Clayton copula with unknown copula parameter. The system is regarded to be operating only if at least $s$($1 \leq s \leq k$) strength variables in the system exceed the random stress. The maximum likelihood estimates (MLE) of unknown parameters and system reliability is established and associated asymptotic confidence interval is constructed using the asymptotic normality property and delta method, and the bootstrap confidence intervals are obtained using the sampling theory. Finally, Monte Carlo simulation is conducted to support the proposed model and methods, and one real data set is analyzed to demonstrate the applicability of our study.

Phased mission reliability analysis of unmanned ship systems

<abstract> <p>With the development of unmanned ships, their use in production is becoming more and more common. However, the unmanned ship work cycle is long and the work environment is complex, and it is still very difficult to calculate the phased mission reliability without unmanned ship. We analyze the unmanned ship phased mission reliability based on the binary decision diagram. Moreover, redundancy is used as the unmanned ship reliability optimization scheme. Considering the resource limitation, and the capacity of unmanned ship, the redundancy allocation scheme of unmanned ship is established. The redundancy allocation scheme is solved by marginal optimization algorithm. Finally, a case study is established to analyze the effectiveness and practicality of the proposed method.</p> </abstract>